%% Genetic Programming Bibliography %%$Revision: 1.8886 $ $Date: 2026/04/15 11:23:36 $ %%Created by W.B.Langdon cs.ucl.ac.nl January 1995 %%Based on J.Koza's GP bibliography of 14 March 1994 %% To add references to your papers see %% ftp://ftp.cs.bham.ac.uk/pub/authors/W.B.Langdon/biblio/ %% size = {84 pages}, %%optional References [AA02] Alexandre P. Alves da Silva and Pedro Jose Abrao. Applications of evolutionary computation in electric power systems. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Greenwood, Hitoshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, pages 1057--1062. IEEE Press, 12-17 May 2002. [AA04] J. Aguilar and J. Altamiranda. A data mining algorithm based on the genetic programming, 2004. [AA05] Wendy Ashlock and Dan Ashlock. Single parent genetic programming. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 2, pages 1172--1179, Edinburgh, UK, 2-5 September 2005. IEEE Press. [AA10] Ala' S. Al-Afeef. Image reconstructing in electrical capacitance tomography of manufacturing processes using genetic programming. Master's thesis, Al-Balqa Applied University, Al-Salt, Jordan, July 2010. [AA11] Wendy Ashlock and Daniel Ashlock. Designing artificial organisms for use in biological simulations. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB 2011), Paris, 11-15 April 2011. [AA12a] H. Md. Azamathulla and Z. Ahmad. Gene-expression programming for transverse mixing coefficient. Journal of Hydrology, 434-435:142--148, April 2012. [AA12b] H. Md. Azamathulla and Z. Ahmad. GP approach for critical submergence of intakes in open channel flows. Journal of Hydroinformatics, 14(4):937--943, October 2012. [AA17a] Hakan Ayral and Songul Albayrak. Effects of population, generation and test case count on grammatical genetic programming for integer lists. Journal of Software, 12(6):483--492, June 2017. [AA17b] Hakan Ayral and Songul Albayrak. Parallel and in-process compilation of individuals for genetic programming on GPU. PeerJ PrePrints, 5:e2936, 2017. [AA21a] Davut Ari and Baris Baykant Alagoz. A genetic programming based pollutant concentration predictor design for urban pollution monitoring based on multi-sensor electronic nose. In 2021 International Conference on Information Technology (ICIT), pages 168--172, July 2021. [AA21b] Davut Ari and Baris Baykant Alagoz. Modeling daily financial market data by using tree-based genetic programming. In 2021 International Conference on Information Technology, ICIT, pages 382--386, Amman, Jordan, 14-15 July 2021. IEEE. [AA21c] Davut Ari and Baris Baykant Alagoz. A review of genetic programming: Popular techniques, fundamental aspects, software tools and applications. Sakarya University Journal of Science, 25(2):397--416, April 2021. [AA22] Davut Ari and Baris Baykant Alagoz. An effective integrated genetic programming and neural network model for electronic nose calibration of air pollution monitoring application. Neural Computing and Applications, 34(15), 2022. [AA23a] Davut Ari and Baris Baykant Alagoz. DEHypGpOls: a genetic programming with evolutionary hyperparameter optimization and its application for stock market trend prediction. Soft Computing, 27(5):2553--2574, March 2023. [AA23b] Davut Ari and Baris Baykant Alagoz. A differential evolutionary chromosomal gene expression programming technique for electronic nose applications. Applied Soft Computing, 136:110093, March 2023. [AA24] Soghra Andaryani and Amin Afkhaminia. Real-time prediction of river ice breakup phenomena: A jittered genetic programming model and wavelet analysis integrating remotely sensed imagery and machine learning. Journal of Hydrology, 644:132097, 2024. [AAA08] Ali Aytek, M Asce, and Murat Alp. An application of artificial intelligence for rainfall-runoff modeling. Journal of Earth System Science, 117(2):145--155, April 2008. [AAA+09] M. Arvaneh, H. Ahmadi, A. Azemi, M. Shajiee, and Z. S. Dastgheib. Prediction of paroxysmal atrial fibrillation by dynamic modeling of the PR interval of ECG. In International Conference on Biomedical and Pharmaceutical Engineering, ICBPE '09, pages 1--5, 2-4 December 2009. [AAA13] H. Md. Azamathulla, Zulfequar Ahmad, and Aminuddin Ab. Ghani. An expert system for predicting manning's roughness coefficient in open channels by using gene expression programming. Neural Computing and Applications, 23(5):1343--1349, 2013. [AAA15] Mohammed Alweshah, Walid Ahmed, and Hamza Aldabbas. Evolution of software reliability growth models: A comparison of auto-regression and genetic programming models. International Journal of Computer Applications, 125(3):20--25, September 2015. [AAA+18] K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. Usman, and M. S. Khan. Intelligent techniques for forecasting electricity consumption of buildings. Energy, 157:886--893, 2018. [AAA+23a] Abdulaziz Alaskar, Ghasan Alfalah, Fadi Althoey, Mohammed Awad Abuhussain, Muhammad Faisal Javed, Ahmed Farouk Deifalla, and Nivin A. Ghamry. Comparative study of genetic programming-based algorithms for predicting the compressive strength of concrete at elevated temperature. Case Studies in Construction Materials, 18:e02199, 2023. [AAA23b] Mehmet Safa Aydogan, Sema Alacali, and Guray Arslan. Prediction of moment redistribution capacity in reinforced concrete beams using gene expression programming. Structures, 47:2209--2219, 2023. [AAA+24a] Hamdan Alanzi, Hamoud Alenezi, Oladayo Adeyi, Abiola J. Adeyi, Emmanuel Olusola, Chee-Yuen Gan, and Olusegun Abayomi Olalere. Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from phyllantus emblica. Journal of Engineering Research, 2024. [AAA+24b] Saad Alatefi, Okorie Ekwe Agwu, Reda Abdel Azim, Ahmad Alkouh, and Iskandar Dzulkarnain. Development of multiple explicit data-driven models for accurate prediction of CO2 minimum miscibility pressure. Chemical Engineering Research and Design, 2024. [AAAD18] Okorie E. Agwu, Julius U. Akpabio, Sunday B. Alabi, and Adewale Dosunmu. Settling velocity of drill cuttings in drilling fluids: A review of experimental, numerical simulations and artificial intelligence studies. Powder Technology, 339:728--746, 2018. [AAB12] Fathi Abid, Wafa Abdelmalek, and Sana Ben Hamida. Dynamic hedging using generated genetic programming implied volatility models. In Sebastian Ventura, editor, Genetic Programming - New Approaches and Successful Applications, chapter 7, pages 141--172. InTech, 2012. [AAB13] Corneliu T. C. Arsene, Denisa Ardevan, and Paul Bulzu. Reverse engineering methodology for bioinformatics based on genetic programming, differential expression analysis and other statistical methods. In Enrico Formenti, Roberto Tagliaferri, and Ernst Wit, editors, CIBB, volume 8452 of Lecture Notes in Computer Science, pages 161--177. Springer, 2013. [AAB+22] Mahmoud Al Najar, Rafael Almar, Erwin W. J. Bergsma, Jean-Marc Delvit, and Dennis G. Wilson. Genetic improvement of shoreline evolution forecasting models. In Bobby R. Bruce, Vesna Nowack, Aymeric Blot, Emily Winter, W. B. Langdon, and Justyna Petke, editors, GI @ GECCO 2022, pages 1916--1923, Boston, USA, 9 July 2022. Association for Computing Machinery. [AAB+23] Mahmoud Al Najar, Rafael Almar, Erwin W. J. Bergsma, Jean-Marc Delvit, and Dennis G. Wilson. Improving a shoreline forecasting model with symbolic regression. In ICLR 2023 Workshop on Tackling Climate Change with Machine Learning, Kigali Rwanda, 4 May 2023. [AABC23] Tasos Asonitis, Richard Allmendinger, Matt Benatan, and Ricardo Climent. SonOpt: understanding the behaviour of bi-objective population-based optimisation algorithms through sound. Genetic Programming and Evolvable Machines, 24:article no. 3, 2023. Special Issue: Evolutionary Computation in Art, Music and Design. [AABC26] Shehu AbdusSalam, Steven Abel, Deaglan Bartlett, and Miguel Crispim Romao. Symbolic regression and differentiable fits in beyond the standard model physics. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 384(2317):20240593, 9 April 2026. [AACL99] John A. Atkinson-Abutridy and Julio R. Carrasco-Leon. An evolutionary model for dynamically controlling a behavior-based autonomous agent. In Scott Brave and Annie S. Wu, editors, Late Breaking Papers at the 1999 Genetic and Evolutionary Computation Conference, pages 16--24, Orlando, Florida, USA, 13 July 1999. [AAd+05] Abdel Latif Abu Dalhoum, Moh'd Al Zoubi, Marina de la Cruz, Alfonso Ortega, and Manuel Alfonseca. A genetic algorithm for solving the p-median problem. In J. Manuel Feliz Teixeira and A. E.Carvalho Brito, editors, European Simulation and Modeling Conference ESM'2005, pages 141--145, Porto, Portugal, October 24-26 2005. http://www.eurosis.org. [AAD11] J. Altamiranda, J. Aguilar, and C. Delamarche. Similarity of amyloid protein motif using an hybrid intelligent system. IEEE Latin America Transactions (Revista IEEE America Latina), 9(5):700--710, September 2011. In Spanish. [AAD13] Junior Altamiranda, Jose Aguilar, and Chistian Delamarche. Comparison and fusion model in protein motifs. In XXXIX Latin American Computing Conference (CLEI 2013), Naiguata, 7-11 October 2013. IEEE. [AAD20a] Mohamed Aliwi, Selcuk Aslan, and Sercan Demirci. Firefly programming for symbolic regression problems. In 2020 28th Signal Processing and Communications Applications Conference (SIU), October 2020. [AAD20b] Dario Alviso, Guillermo Artana, and Thomas Duriez. Prediction of biodiesel physico-chemical properties from its fatty acid composition using genetic programming. Fuel, 264:116844, 2020. [AAd20c] Felipe S. P. Andrade, Claus Aranha, and Ricardo da Silva Torres. On the use of predation to shape evolutionary computation. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pages 117--124, December 2020. [AAD21] Okorie Ekwe Agwu, Julius Udoh Akpabio, and Adewale Dosunmu. Modeling the downhole density of drilling muds using multigene genetic programming. Upstream Oil and Gas Technology, 6:100030, 2021. [AAE18] Mohamed Abdelwhab, A. A. Abouelsoud, and Ahmed M. R. Fath Elbab. Tackling dead end scenarios by improving follow gap method with genetic programming. In 2018 57th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), pages 1566--1571, Nara, Japan, September 2018. [AAET16] Ameen Abdelmutalab, Khaled Assaleh, and Mohamed El-Tarhuni. Automatic modulation classification based on high order cumulants and hierarchical polynomial classifiers. Physical Communication, 21:10--18, 2016. [AAFJG11] Alireza Ahangar-Asr, Asaad Faramarzi, Akbar A. Javadi, and Orazio Giustolisi. Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression. Engineering Computation, 28(4):492--507, 2011. [AAG11] Thomas Ackling, Bradley Alexander, and Ian Grunert. Evolving patches for software repair. In Natalio Krasnogor, Pier Luca Lanzi, Andries Engelbrecht, David Pelta, Carlos Gershenson, Giovanni Squillero, Alex Freitas, Marylyn Ritchie, Mike Preuss, Christian Gagne, Yew Soon Ong, Guenther Raidl, Marcus Gallager, Jose Lozano, Carlos Coello-Coello, Dario Landa Silva, Nikolaus Hansen, Silja Meyer-Nieberg, Jim Smith, Gus Eiben, Ester Bernado-Mansilla, Will Browne, Lee Spector, Tina Yu, Jeff Clune, Greg Hornby, Man-Leung Wong, Pierre Collet, Steve Gustafson, Jean-Paul Watson, Moshe Sipper, Simon Poulding, Gabriela Ochoa, Marc Schoenauer, Carsten Witt, and Anne Auger, editors, GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 1427--1434, Dublin, Ireland, 12-16 July 2011. ACM. [AAG+22] Rashed Alsharif, Mehrdad Arashpour, Emadaldin Mohammadi Golafshani, M. Reza Hosseini, Victor Chang, and Jenny Zhou. Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings. Journal of Building Engineering, 46:103846, 2022. [AAGA11] Amir Hossein Alavi, Pejman Aminian, Amir Hossein Gandomi, and Milad Arab Esmaeili. Genetic-based modeling of uplift capacity of suction caissons. Expert Systems with Applications, 38(10):12608--12618, 15 September 2011. [AAGM11] Amir Hossein Alavi, Mahmoud Ameri, Amir Hossein Gandomi, and Mohammad Reza Mirzahosseini. Formulation of flow number of asphalt mixes using a hybrid computational method. Construction and Building Materials, 25(3):1338--1355, March 2011. [AAH15] K. P. Amber, M. W. Aslam, and S. K. Hussain. Electricity consumption forecasting models for administration buildings of the UK higher education sector. Energy and Buildings, 90:127--136, 2015. [AAH20] Seyed Mohammad Hossein Hosseini Amini, Mohammad Abdollahi, and Maryam Amir Haeri. Rule-centred genetic programming (RCGP): an imperialist competitive approach. Appl. Intell., 50(8):2589--2609, 2020. [AAH21] Jalal Al-Afandi and Andras Horvath. Adaptive gene level mutation. Algorithms, 14(1), 2021. [AAH25] Khulud Alharthi, S Zahraa Abdallah, and Sabine Hauert. Ghost swarms: Learning swarm rules from environmental changes alone. In Bing Xue, Luca Manzoni, and Illya Bakurov, editors, European Conference on Genetic Programming, EuroGP 2025, volume 15609 of LNCS, pages 1--17, Trieste, 23-25 April 2025. Springer Nature. [AAHM15] Habib Akbari-Alashti, Omid Bozorg Haddad, and Miguel A. Marino. Application of fixed length gene genetic programming (FLGGP) in hydropower reservoir operation. Water Resources Management, 29(9), 2015. [AAJ23a] Umair Ahmed, Fakhre Ali, and Ian Jennions. Acoustic monitoring of an aircraft auxiliary power unit. ISA Transactions, 2023. [AAJ+23b] Muna Albalushi, Rasha Al Jassim, Karan Jetly, Raya Al Khayari, and Hilal Al Maqbali. Optimizing diabetes predictive modeling with automated decision trees. In 2023 IEEE Smart World Congress (SWC), August 2023. [AAJ+24] Rasha S. Al Jassim, Shqran Al Mansoory, Karan Jetly, Hilal Ali Abdullah AlMaqbali, and Muna Mohammed Albalushi. Enhancing tourism performance in oman: A case study using correlation-guided linear genetic programming decision tree (c-LGPDT). In 2024 10th International Conference on Control, Decision and Information Technologies (CoDIT), pages 1655--1660, July 2024. [AAJA24] Rasha S. Al Jassim, Shqran Al Mansoory, Karan Jetly, and Hilal AlMaqbali. Enhancing hotel performance prediction in oman's tourism industry: Insights from machine learning, feature analysis, and predictive factors. In 2024 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), May 2024. [AAJJ23] Alireza Ahangar-Asr, A. Johari, and Akbar A. Javadi. An evolutionary-based polynomial regression modeling approach to predicting discharge flow rate under sheet piles. Engineering with Computers, 39(6):4093--4101, 2023. [AAK04] B. Ali, A. E. A. Almaini, and T. Kalganova. Evolutionary algorithms and theirs use in the design of sequential logic circuits. Genetic Programming and Evolvable Machines, 5(1), March 2004. [AAK+24] Mahmoud Alrsai, Ala' Alsahalen, Hassan Karampour, Mohammad Alhawamdeh, and Omar Alajarmeh. Integrated finite element analysis and machine learning approach for propagation pressure prediction in hybrid steel-CFRP subsea pipelines. Ocean Engineering, 311:118808, 2024. [AAM+14] Joshua E. Auerbach, Deniz Aydin, Andrea Maesani, Przemyslaw M. Kornatowski, Titus Cieslewski, Gregoire Heitz, Pradeep R. Fernando, Ilya Loshchilov, Ludovic Daler, and Dario Floreano. RoboGen: Robot generation through artificial evolution. In Hiroki Sayama, John Rieffel, Sebastian Risi, Rene Doursat, and Hod Lipson, editors, Proceedings of the Fourteenth International Conference of the Synthesis and Simulation of Living Systems, ALIFE 14, Complex Adaptive Systems, pages 136--137, New York, 30 July-2 August 2014. MIT Press. [AAM19] Daniel Ashlock, Wendy Ashlock, and James Montgomery. Implementing phenotypic plasticity with an adaptive generative representation. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), Siena, Italy, 9-11 July 2019. [AAM23] Mohammed Al-Aghbari and Ashish M. Gujarathi. Hybrid approach of using bi-objective genetic programming in well control optimization of waterflood management. Geoenergy Science and Engineering, 228:211967, 2023. [AAN20] Rida Azmi, Hicham Amar, and Abderrahim Norelyaqine. Generate knowledge base from very high spatial resolution satellite image using robust classification rules and genetic programming. 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Taiwo, Sunday O. Oladunni, and Kelechi N. Akatobi. Process integration for food colorant production from hibiscus sabdariffa calyx: A case of multi-gene genetic programming (MGGP) model and techno-economics. Alexandria Engineering Journal, 61(7):5235--5252, 2022. [AAP19a] Alok Adhikari, N. Adhikari, and K. C. Patra. Genetic programming: A complementary approach for discharge modelling in smooth and rough compound channels. Journal of The Institution of Engineers (India): Series A, 100(3):395--405, September 2019. [AAP19b] Alok Adhikari, Nibedita Adhikari, and K. C. Patra. Shear force analysis and modeling for discharge estimation using numerical and GP for compound channels. In Soft Computing in Data Analytics. Springer, 2019. [AAPd12] Felipe S. P. Andrade, Jurandy Almeida, Helio Pedrini, and Ricardo da S. Torres. Fusion of local and global descriptors for content-based image and video retrieval. In 17th Iberoamerican Congress on Pattern Recognition, pages 845--853, Buenos Aires, Argentina, 2012. [AAR+04] R. Muhammad Atif Azad, Ali R. Ansari, Conor Ryan, Michael Walsh, and Tim McGloughlin. An evolutionary approach to wall sheer stress prediction in a grafted artery. Applied Soft Computing, 4(2):139--148, May 2004. [AAS09] Dilip Ahalpara, Siddharth Arora, and M Santhanam. Genetic programming based approach for synchronization with parameter mismatches in eeg. In Leonardo Vanneschi, Steven Gustafson, Alberto Moraglio, Ivanoe De Falco, and Marc Ebner, editors, Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, volume 5481 of LNCS, pages 13--24, Tuebingen, April 15-17 2009. Springer. [AASAR10] Alaa Al-Afeef, Alaa F. Sheta, and Adnan Al-Rabea. Image reconstruction of a metal fill industrial process using genetic programming. In 10th International Conference on Intelligent Systems Design and Applications (ISDA), 2010, pages 12--17, Cairo, 29 November-1 December 2010. [AASB18] Ibrahim Z. Abdelbaky, Ahmed F. Al-Sadek, and Amr A. Badr. Applying machine learning techniques for classifying cyclin-dependent kinase inhibitors. International Journal of Advanced Computer Science and Applications, 9(11):229--235, 2018. [AASP18] Sachindra Dhanapala Arachchige, Khandakar Ahmed, S Shahid, and B. J. C Perera. Cautionary note on the use of genetic programming in statistical downscaling. International Journal of Climatology, 38(8):3449--3465, June 2018. SHORT COMMUNICATION. [AASR11] Alaa Al-Afeef, Alaa Sheta, and Adnan Rabea. Image Reconstruction of a Manufacturing Process: A Genetic Programming Approach. Lambert Academic Publishing, 1 edition, April 2011. [AASW21] Muhammad Shabbir Abbasi, Harith Al-Sahaf, and Ian Welch. Automated behavior-based malice scoring of ransomware using genetic programming. 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[AASX+19] Shima Afzali, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang. Genetic programming for feature selection and feature combination in salient object detection. In Paul Kaufmann and Pedro A. Castillo, editors, 22nd International Conference, EvoApplications 2019, volume 11454 of LNCS, pages 308--324, Leipzig, Germany, 24-26 April 2019. Springer Verlag. [AASX+21] Shima Afzali Vahed Moghaddam, Harith Al-Sahaf, Bing Xue, Christopher Hollitt, and Mengjie Zhang. An automatic feature construction method for salient object detection: A genetic programming approach. Expert Systems with Applications, 186:115726, 2021. [AASXZ] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Genetic programming for malignancy diagnosis from breast cancer histopathological images: A feature learning approach. IEEE Transactions on Emerging Topics in Computational Intelligence. [AASXZ22a] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Automatically diagnosing skin cancers from multimodality images using two-stage genetic programming. IEEE Transactions on Cybernetics, 2022. [AASXZ22b] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Genetic programming for automatic skin cancer image classification. Expert Systems with Applications, 197:116680, 2022. [AASXZ23] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. A new genetic programming representation for feature learning in skin cancer detection. In Sara Silva, Luis Paquete, Leonardo Vanneschi, Nuno Lourenco, Ales Zamuda, Ahmed Kheiri, Arnaud Liefooghe, Bing Xue, Ying Bi, Nelishia Pillay, Irene Moser, Arthur Guijt, Jessica Catarino, Pablo Garcia-Sanchez, Leonardo Trujillo, Carla Silva, and Nadarajen Veerapen, editors, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, GECCO '23, pages 707--710, Lisbon, Portugal, 15-19 July 2023. Association for Computing Machinery. [AASXZ24a] Qurrat UI Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Exploring genetic programming models in Computer-Aided diagnosis of skin cancer images. In Bing Xue, editor, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 30 June - 5 July 2024. IEEE. [AASXZ24b] Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, and Mengjie Zhang. Automatically evolving interpretable feature vectors using genetic programming for an ensemble classifier in skin cancer detection. IEEE Computational Intelligence Magazine, 19(3):26--41, August 2024. [AAT03] A. F. Ashour, L. F. Alvarez, and V. V. Toropov. Empirical modelling of shear strength of RC deep beams by genetic programming. Computers and Structures, 81(5):331--338, March 2003. [AAT14] Gholamreza Ghodrati Amiri, Mohamad Shamekhi Amiri, and Zahra Tabrizian. Ground motion prediction equations (GMPEs) for elastic response spectra in the iranian plateau using gene expression programming (GEP). Journal of Intelligent and Fuzzy Systems, 26(6):2825--2839, 2014. [AAY19] Joselito Yam II Alcaraz, Kunal Ahluwalia, and Swee-Hock Yeo. Predictive models of Double-Vibropolishing in bowl system using artificial intelligence methods. Journal of Manufacturing and Materials Processing, 3(1), 2019. [AAZ+08] H. Md Azamathulla, A. Ab. Ghani, N. A. Zakaria, S. H. Lai, C. K. Chang, C. S. Leow, and Z. Abuhasan. Genetic programming to predict ski-jump bucket spill-way scour. Journal of Hydrodynamics, Ser. B, 20(4):477--484, August 2008. [AAZG10] H. Md. Azamathulla, Aminuddin Ab Ghani, Nor Azazi Zakaria, and Aytac Guven. Genetic programming to predict bridge pier scour. Journal of Hydraulic Engineering, 136(3):165--169, 2010. [AB98] M. Ahluwalia and L. Bull. Co-evolving functions in genetic programming: Dynamic adf creation using glib. In V. William Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, volume 1447 of LNCS, pages 809--818, Mission Valley Marriott, San Diego, California, USA, 25-27 March 1998. Springer-Verlag. [AB99a] Manu Ahluwalia and Larry Bull. Coevolving functions in genetic programming: Classification using k-nearest-neighbour. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 947--952, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [AB99b] Manu Ahluwalia and Larry Bull. A genetic programming-based classifier system. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 11--18, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [AB00] Douglas A. Augusto and Helio J. C. Barbosa. Symbolic regression via genetic programming. In VI Brazilian Symposium on Neural Networks (SBRN'00), page 173, Rio de Janeiro, RJ, Brazil, January 22-25 2000. IEEE. VI Simposio Brasileiro de Redes Neurais. [AB01] Manu Ahluwalia and Larry Bull. Coevolving functions in genetic programming. Journal of Systems Architecture, 47(7):573--585, July 2001. [AB03] Daniel A. Ashlock and Kenneth M. Bryden. Thermal agents: An application of genetic programming to virtual engineering. In Ruhul Sarker, Robert Reynolds, Hussein Abbass, Kay Chen Tan, Bob McKay, Daryl Essam, and Tom Gedeon, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 1340--1347, Canberra, 8-12 December 2003. IEEE Press. [AB06] Daniel Ashlock and Kenneth M. Bryden. Function stacks, GBEAs, and crossover for the parity problem. In Cihan H. Dagli, Anna L. Buczak, David L. 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Johnson, Elena Marchiori, Jean-Arcady Meyer, and Martin Middendorf, editors, Applications of Evolutionary Computing, EvoWorkshops2003: EvoBIO, EvoCOP, EvoIASP, EvoMUSART, EvoROB, EvoSTIM, volume 2611 of LNCS, pages 44--53, University of Essex, England, UK, 14-16 April 2003. Springer-Verlag. [HB05] Robin Harper and Alan Blair. A structure preserving crossover in grammatical evolution. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 3, pages 2537--2544, Edinburgh, UK, 2-5 September 2005. IEEE Press. [HB06a] Robin Harper and Alan Blair. Dynamically defined functions in grammatical evolution. 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In Marc Ebner, Michael O'Neill, Anik'o Ek'art, Leonardo Vanneschi, and Anna Isabel Esparcia-Alc'azar, editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 90--101, Valencia, Spain, 11-13 April 2007. Springer. [HB08a] S. Harding and W. Banzhaf. Genetic programming on GPUs for image processing. International Journal of High Performance Systems Architecture, 1(4):231--240, 2008. [HB08b] Ting Hu and Wolfgang Banzhaf. Evolvability and acceleration in evolutionary computation. Technical Report 2008-04, Department of Computer Science, Memorial University of Newfoundland, St. John's, NL, Canada A1B 3X5, October 2008. [HB08c] Ting Hu and Wolfgang Banzhaf. Measuring rate of evolution in genetic programming using amino acid to synonymous substitution ratio ka/ks. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev Kumar, Fernando G. Lobo, Julian Francis Miller, Jason Moore, Frank Neumann, Martin Pelikan, Jordan Pollack, Kumara Sastry, Kenneth Stanley, Adrian Stoica, El-Ghazali Talbi, and Ingo Wegener, editors, GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pages 1337--1338, Atlanta, GA, USA, 12-16 July 2008. ACM. [HB08d] Ting Hu and Wolfgang Banzhaf. Nonsynonymous to synonymous substitution ratio ka/ks: Measurement for rate of evolution in evolutionary computation. In Gunter Rudolph, Thomas Jansen, Simon Lucas, Carlo Poloni, and Nicola Beume, editors, Parallel Problem Solving from Nature - PPSN X, volume 5199 of LNCS, pages 448--457, Dortmund, 13-17 September 2008. Springer. [HB09a] Simon L. Harding and Wolfgang Banzhaf. Distributed genetic programming on GPUs using CUDA. 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The role of population size in rate of evolution in genetic programming. In Leonardo Vanneschi, Steven Gustafson, Alberto Moraglio, Ivanoe De Falco, and Marc Ebner, editors, Proceedings of the 12th European Conference on Genetic Programming, EuroGP 2009, volume 5481 of LNCS, pages 85--96, Tuebingen, April 15-17 2009. Springer. [HB10] Ting Hu and Wolfgang Banzhaf. Evolvability and speed of evolutionary algorithms in light of recent developments in biology. Journal of Artificial Evolution and Applications, 2010:Article ID 568375, 2010. Review Article. [HB11a] Simon Harding and Wolfgang Banzhaf. Implementing cartesian genetic programming classifiers on graphics processing units using gpu.net. In Simon Harding, W. B. Langdon, Man Leung Wong, Garnett Wilson, and Tony Lewis, editors, GECCO 2011 Computational intelligence on consumer games and graphics hardware (CIGPU), pages 463--470, Dublin, Ireland, 12-16 July 2011. ACM. [HB11b] Simon L. Harding and Wolfgang Banzhaf. 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In Rick Riolo, Bill Worzel, Brian Goldman, and Bill Tozier, editors, Genetic Programming Theory and Practice XIV, pages 101--117, Ann Arbor, USA, 19-21 May 2016. Springer. [HB16b] Ting Hu and Wolfgang Banzhaf. Quantitative analysis of evolvability using vertex centralities in phenotype network. In Tobias Friedrich, editor, GECCO '16: Proceedings of the 2016 Annual Conference on Genetic and Evolutionary Computation, pages 733--740, Denver, USA, 20-24 July 2016. ACM. Nominated for best paper. [HB18] Nhat-Duc Hoang and Dieu Tien Bui. Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with gis: a case study in vietnam. Natural Hazards, 92:1871--1887, 2018. [HB20] William T. Hale and George M. Bollas. Symbolic regression of uncertainty-resilient inferential sensors for fault diagnostics. IFAC-PapersOnLine, 53(2):11446--11451, 2020. 21st IFAC World Congress. [HBA12] Gerard David Howard, Larry Bull, and Andrew Adamatzky. 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In 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pages 1221--1226, July 2016. [HBBR16b] Hmida Hmida, Sana Ben Hamida, Amel Borgi, and Marta Rukoz. Sampling methods in genetic programming learners from large datasets: A comparative study. In Plamen Angelov, Yannis Manolopoulos, Lazaros S. Iliadis, Asim Roy, and Marley M. B. R. Vellasco, editors, INNS Conference on Big Data, volume 529 of Advances in Intelligent Systems and Computing, pages 50--60, 2016. [HBBR18] Hmida Hmida, Sana Ben Hamida, Amel Borgi, and Marta Rukoz. Scale genetic programming for large data sets: Case of Higgs bosons classification. Procedia Computer Science, 126:302--311, 2018. 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Haraldsson, Markus Wagner, Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stuetzle, David Walker, Matt Johns, Nick Ross, Ed Keedwell, Masaya Nakata, Anthony Stein, Takato Tatsumi, Nadarajen Veerapen, Arnaud Liefooghe, Sebastien Verel, Gabriela Ochoa, Stephen Smith, Stefano Cagnoni, Robert M. Patton, William La Cava, Randal Olson, Patryk Orzechowski, Ryan Urbanowicz, Akira Oyama, Koji Shimoyama, Hemant Kumar Singh, Kazuhisa Chiba, Pramudita Satria Palar, Alma Rahat, Richard Everson, Handing Wang, Yaochu Jin, Marcus Gallagher, Mike Preuss, Olivier Teytaud, Fernando Lezama, Joao Soares, and Zita Vale, editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 1478--1486, Prague, Czech Republic, 13-17 July 2019. ACM. [HPW+99] William H. Hsu, William M. Pottenger, Michael Welge, Jie Wu, and Ting-Hao Yang. Genetic algorithms for attribute synthesis in large-scale data mining. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, page 1783, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [HPWS20a] Thomas Helmuth, Edward Pantridge, Grace Woolson, and Lee Spector. Genetic source sensitivity and transfer learning in genetic programming. In Josh Bongard, Juniper Lovato, Laurent Hebert-Dufresne, Radhakrishna Dasari, and Lisa Soros, editors, 2020 Conference on Artificial Life, pages 303--311, Online, 13-18 July 2020. Massachusetts Institute of Technology. [HPWS20b] Thomas Helmuth, Edward Pantridge, Grace Woolson, and Lee Spector. Transfer learning of genetic programming instruction sets. In Richard Allmendinger, Hugo Terashima Marin, Efren Mezura Montes, Thomas Bartz-Beielstein, Bogdan Filipic, Ke Tang, David Howard, Emma Hart, Gusz Eiben, Tome Eftimov, William La Cava, Boris Naujoks, Pietro Oliveto, Vanessa Volz, Thomas Weise, Bilel Derbel, Ke Li, Xiaodong Li, Saul Zapotecas, Qingfu Zhang, Rui Wang, Ran Cheng, Guohua Wu, Miqing Li, Hisao Ishibuchi, Jonathan Fieldsend, Ozgur Akman, Khulood Alyahya, Juergen Branke, John R. Woodward, Daniel R. Tauritz, Marco Baioletti, Josu Ceberio Uribe, John McCall, Alfredo Milani, Stefan Wagner, Michael Affenzeller, Bradley Alexander, Alexander (Sandy) Brownlee, Saemundur O. Haraldsson, Markus Wagner, Nayat Sanchez-Pi, Luis Marti, Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stuetzle, Matthew Johns, Nick Ross, Ed Keedwell, Herman Mahmoud, David Walker, Anthony Stein, Masaya Nakata, David Paetzel, Neil Vaughan, Stephen Smith, Stefano Cagnoni, Robert M. Patton, Ivanoe De Falco, Antonio Della Cioppa, Umberto Scafuri, Ernesto Tarantino, Akira Oyama, Koji Shimoyama, Hemant Kumar Singh, Kazuhisa Chiba, Pramudita Satria Palar, Alma Rahat, Richard Everson, Handing Wang, Yaochu Jin, Erik Hemberg, Riyad Alshammari, Tokunbo Makanju, Fuijimino-shi, Ivan Zelinka, Swagatam Das, Ponnuthurai Nagaratnam, and Roman Senkerik, editors, Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, GECCO '20, pages 241--242, internet, July 8-12 2020. Association for Computing Machinery. [HQZ+15] Qin Hu, Aisong Qin, Qinghua Zhang, Guoxi Sun, and Longqiu Shao. Application of an information fusion method to compound fault diagnosis of rotating machinery. In The 27th Chinese Control and Decision Conference (2015 CCDC), pages 3859--3864, May 2015. [HR99a] Laura J. Harrell and S. Ranji Ranjithan. Evaluation of alternative penalty function implementations in a watershed management design problem. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1551--1558, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [HR99b] Emma Hart and Peter Ross. An immune system approach to scheduling in changing environments. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1559--1566, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [HR99c] Daniel Howard and Simon C. Roberts. Evolving object detectors for infrared imagery: a comparison of texture analysis against simple statistics. In Kaisa Miettinen, Marko M. Makela, Pekka Neittaanmaki, and Jacques Periaux, editors, Evolutionary Algorithms in Engineering and Computer Science, pages 79--86, Jyvaskyla, Finland, 30 May - 3 June 1999. John Wiley & Sons. [HR99d] Daniel Howard and Simon C. Roberts. A staged genetic programming strategy for image analysis. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1047--1052, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [HR00] Daniel Howard and Simon C. Roberts. Evolution of mesh refinement rules for impact dynamics. In Proceedings of the 2000 Congress on Evolutionary Computation CEC00, pages 1297--1303, La Jolla Marriott Hotel La Jolla, California, USA, 6-9 July 2000. IEEE Press. [HR01] Daniel Howard and Simon C. Roberts. Genetic programming solution of the convection-diffusion equation. In Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 34--41, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann. [HR02a] Yoon-Seok Hong and Michael R. Rosen. Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling. Journal of Hydrology, 259(1-4):89--104, 2002. [HR02b] Daniel Howard and Simon C. Roberts. Application of genetic programming to motorway traffic modelling. In W. B. Langdon, E. Cant'u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 1097--1104, New York, 9-13 July 2002. Morgan Kaufmann Publishers. [HR02c] Daniel Howard and Simon C. Roberts. The prediction of journey times on motorways using genetic programming. In Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, and G"unther Raidl, editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN, volume 2279 of LNCS, pages 210--221, Kinsale, Ireland, 3-4 April 2002. Springer-Verlag. [HR03a] Emma Hart and Peter Ross. Exploiting the analogy between the immune system and sparse distributed memories. Genetic Programming and Evolvable Machines, 4(4):333--358, December 2003. [HR03b] Javier Hervas and Paul L. Rosin. Image thresholding for landslide detection by genetic programming, January 02 2003. [HR03c] Adam Hewgill and Brian J. Ross. The evolution of 3D procedural textures. In Bart Rylander, editor, Genetic and Evolutionary Computation Conference Late Breaking Papers, pages 146--147, Chicago, USA, 12 July 2003. [HR03d] Adam Hewgill and Brian J. Ross. Procedural 3D texture synthesis using genetic programming. Technical Report CS-03-06, Brock University, Department of Computer Science, St. Catharines, Ontario, Canada L2S 3A1, April 2003 2003. [HR03e] Chien-Feng Huang and Luis M. Rocha. Exploration of RNA editing and design of robust genetic algorithms. In Ruhul Sarker, Robert Reynolds, Hussein Abbass, Kay Chen Tan, Bob McKay, Daryl Essam, and Tom Gedeon, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 2799--2806, Canberra, 8-12 December 2003. IEEE Press. [HR04a] Adam Hewgill and Brian J. Ross. Procedural 3D texture synthesis using genetic programming. Computers and Graphics, 28(4):569--584, August 2004. [HR04b] Daniel Howard and Simon C. Roberts. Incident detection on highways. In Una-May O'Reilly, Tina Yu, Rick L. Riolo, and Bill Worzel, editors, Genetic Programming Theory and Practice II, chapter 16, pages 263--282. Springer, Ann Arbor, 13-15 May 2004. [HR07] K. L. Holladay and K. A. Robbins. Evolution of signal processing algorithms using vector based genetic programming. In S. Sanei, J. A. Chambers, J. McWhlrter, Y. Hicks, and A. G. Constantinlides, editors, 15th International Conference on Digital Signal Processing, pages 503--506, Cardiff, UK, 1-4 July 2007. IEEE. [HR12] Daniel Howard and Conor Ryan. Testing a novel attribute grammar genetic programming algorithm. In Geuk Lee, Daniel Howard, Jeong Jin Kang, and Dominik Slezak, editors, 6th International Conference Convergence and Hybrid Information Technology, ICHIT 2012, volume 7425 of Lecture Notes in Computer Science, pages 224--231, Daejeon, Korea, August 23-25 2012. Springer. [HR13] Adrian Harrington and Brian J. Ross. Generative representations for artificial architecture and passive solar performance. In Luis Gerardo de la Fraga, editor, 2013 IEEE Conference on Evolutionary Computation, volume 1, pages 537--545, Cancun, Mexico, June 20-23 2013. [HRB98] Daniel Howard, Simon C. Roberts, and Richard Brankin. Target detection in sar imagery by genetic programming. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, pages 67--75, University of Wisconsin, Madison, Wisconsin, USA, 22-25 July 1998. Stanford University Bookstore. [HRB99a] Daniel Howard, Simon C. Roberts, and Richard Brankin. Evolution of ship detectors for satellite SAR imagery. In Riccardo Poli, Peter Nordin, William B. Langdon, and Terence C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP'99, volume 1598 of LNCS, pages 135--148, Goteborg, Sweden, 26-27 May 1999. Springer-Verlag. [HRB99b] Daniel Howard, Simon C. Roberts, and Richard Brankin. Target detection in sar imagery by genetic programming. Advances in Engineering Software, 30(5):303--311, May 1999. [Hrb14] Radek Hrbacek. Bent functions synthesis on Intel Xeon Phi coprocessor. In Petr Hlineny, Zdenek Dvorak, Jiri Jaros, Jan Kofron, Jan Korenek, Petr Matula, and Karel Pala, editors, 9th International Doctoral Workshop Mathematical and Engineering Methods in Computer Science, MEMICS 2014, volume 8934 of Lecture Notes in Computer Science, pages 88--99, Telc, Czech Republic, October 17-19 2014. Springer. Revised Selected Papers. [Hrb15] Radek Hrbacek. Parallel multi-objective evolutionary design of approximate circuits. In Sara Silva, Anna I Esparcia-Alcazar, Manuel Lopez-Ibanez, Sanaz Mostaghim, Jon Timmis, Christine Zarges, Luis Correia, Terence Soule, Mario Giacobini, Ryan Urbanowicz, Youhei Akimoto, Tobias Glasmachers, Francisco Fernandez de Vega, Amy Hoover, Pedro Larranaga, Marta Soto, Carlos Cotta, Francisco B. Pereira, Julia Handl, Jan Koutnik, Antonio Gaspar-Cunha, Heike Trautmann, Jean-Baptiste Mouret, Sebastian Risi, Ernesto Costa, Oliver Schuetze, Krzysztof Krawiec, Alberto Moraglio, Julian F. Miller, Pawel Widera, Stefano Cagnoni, JJ Merelo, Emma Hart, Leonardo Trujillo, Marouane Kessentini, Gabriela Ochoa, Francisco Chicano, and Carola Doerr, editors, GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pages 687--694, Madrid, Spain, 11-15 July 2015. ACM. [Hrb17] Radek Hrbacek. Automated Multi-Objective Parallel Evolutionary Circuit Design and Approximation. PhD thesis, Department of Computer Systems, Faculty of Information Tech-nology, Brno University of Technology, Brno, Czech Republic, 2017. [HRC05] Emma Hart, Peter Ross, and David Corne. Evolutionary scheduling: A review. Genetic Programming and Evolvable Machines, 6(2):191--220, June 2005. Early and seminal work which applied evolutionary computing methods to scheduling problems from 1985 onwards laid a strong and exciting foundation for the work which has been reported over the past decade or so. A survey of the current state-of-the-art was produced in 1999 for the European Network of Excellence on Evolutionary Computing EVONET. [HRC11] Daniel Howard, Conor Ryan, and J. J. Collins. Attribute grammar genetic programming algorithm for automatic code parallelization. In Geuk Lee, Daniel Howard, and Dominik Slezak, editors, Proceedings of the 5th International Conference on Convergence and Hybrid Information Technology, ICHIT 2011, volume 6935 of Lecture Notes in Computer Science, pages 250--257, Daejeon, Korea, September 22-24 2011. Springer. [HRG05] Jianjun Hu, Ronald C. Rosenberg, and Erik D. Goodman. Domain specificity of genetic programming based automated synthesis: a case study with synthesis of mechanical vibration absorbers. In Tina Yu, Rick L. Riolo, and Bill Worzel, editors, Genetic Programming Theory and Practice III, volume 9 of Genetic Programming, chapter 18, pages 275--290. Springer, Ann Arbor, 12-14 May 2005. [HRHA20] James Alexander Hughes, Ryan E. R. Reid, Sheridan Houghten, and Ross E. Andersen. Using genetic programming to investigate a novel model of resting energy expenditure for bariatric surgery patients. In 2020 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), October 2020. [HRLC+22] Leonardo Herrera, M. C. Rodriguez-Linan, Eddie Clemente, Marlen Meza-Sanchez, and Luis Monay-Arredondo. Evolved extended Kalman filter for first-order dynamical systems with unknown measurements noise covariance. Applied Soft Computing, 115:108174, 2022. [HRM+06] Amaury Hazan, Rafael Ramirez, Esteban Maestre, Alfonso Perez, and Antonio Pertusa. Modelling expressive performance: a regression tree approach based on strongly typed genetic programming. In Franz Rothlauf, Jurgen Branke, Stefano Cagnoni, Ernesto Costa, Carlos Cotta, Rolf Drechsler, Evelyne Lutton, Penousal Machado, Jason H. Moore, Juan Romero, George D. Smith, Giovanni Squillero, and Hideyuki Takagi, editors, Applications of Evolutionary Computing, EvoWorkshops2006: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoInteraction, EvoMUSART, EvoSTOC, volume 3907 of LNCS, pages 676--687, Budapest, 10-12 April 2006. Springer Verlag. [HRM+16] Roslan Hashim, Chandrabhushan Roy, Shervin Motamedi, Shahaboddin Shamshirband, Dalibor Petkovic, Milan Gocic, and Siew Cheng Lee. Selection of meteorological parameters affecting rainfall estimation using neuro-fuzzy computing methodology. Atmospheric Research, 171:21--30, 2016. [HRMS21] Alexander Hammer, Wolfgang Roland, Christian Marschik, and Georg Steinbichler. Predicting the co-extrusion flow of non-newtonian fluids through rectangular ducts - a hybrid modeling approach. Journal of Non-Newtonian Fluid Mechanics, 295:104618, 2021. [HRO16] Erik Hemberg, Jacob Rosen, and Una-May O'Reilly. Investigating multi population competetive coevolution for anticipating of tax evasion. In Rick Riolo, Bill Worzel, Brian Goldman, and Bill Tozier, editors, Genetic Programming Theory and Practice XIV, pages 35--51, Ann Arbor, USA, 19-21 May 2016. Springer. [HRR02a] Daniel Howard, Simon C. Roberts, and Conor Ryan. The boru data crawler for object detection tasks in machine vision. In Stefano Cagnoni, Jens Gottlieb, Emma Hart, Martin Middendorf, and G"unther Raidl, editors, Applications of Evolutionary Computing, Proceedings of EvoWorkshops2002: EvoCOP, EvoIASP, EvoSTim/EvoPLAN, volume 2279 of LNCS, pages 222--232, Kinsale, Ireland, 3-4 April 2002. Springer-Verlag. [HRR02b] Daniel Howard, Simon C. Roberts, and Conor Ryan. Machine vision: Exploring context with genetic programming. In W. B. Langdon, E. Cant'u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 756--763, New York, 9-13 July 2002. Morgan Kaufmann Publishers. [HRR06] Daniel Howard, Simon C. Roberts, and Conor Ryan. Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance. Pattern Recognition Letters, 27(11):1275--1288, August 2006. Evolutionary Computer Vision and Image Understanding. [HRRB08] Daniel Howard, Simon C. Roberts, Conor Ryan, and Adrian Brezulianu. Textural classification of mammographic parenchymal patterns with the sonnet selforganizing neural network. Journal of Biomedicine and Biotechnology, 2008:526343, July 22 2008. [HRS13] Georges Hardier, Clement Roos, and Cedric Seren. Creating sparse rational approximations for linear fractional representations using genetic programming. In Pedro M. Ferreira, editor, 3rd IFAC International Conference on Intelligent Control and Automation Science, ICONS 2013, pages 393--398, Sichuan, Chengdu, China, September 2-4 2013. International Federation of Automatic Control. [HRv07] Kenneth Holladay, Kay Robbins, and Jeffery von Ronne. Fifth: A stack based gp language for vector processing. In Marc Ebner, Michael O'Neill, Anik'o Ek'art, Leonardo Vanneschi, and Anna Isabel Esparcia-Alc'azar, editors, Proceedings of the 10th European Conference on Genetic Programming, volume 4445 of Lecture Notes in Computer Science, pages 102--113, Valencia, Spain, 11-13 April 2007. Springer. [HRW99] Robert B. Heckendorn, Soraya Rana, and Darrell Whitley. Polynomial time summary statistics for a generalization of maxsat. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 281--288, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [HRW+13] Tales Heimfarth, Renato Resende Ribeiro de Oliveira, Raphael Winckler de Bettio, Ariel Felipe Ferreira Marques, and Claudio Fabiano Motta Toledo. Automatic generation and configuration of wireless sensor networks applications with genetic programming. In 16th IEEE International Symposium on Object/Component/Service-O* *riented Real-Time Distributed Computing (ISORC 2013), June 2013. [HRW+15] Erik Hemberg, Jacob Rosen, Geoff Warner, Sanith Wijesinghe, and Una-May O'Reilly. Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood. In Katie Atkinson and Ted Sichelman, editors, Proceedings of the 15th International Conference on Artificial Intelligence and Law, ICAIL-2015, pages 79--88, San Diego, USA, 2015. ACM. [HRW+16] Erik Hemberg, Jacob B. 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[Kor16b] Jyothi Korra. Finding optimal combination of kernels using genetic programming. ArXiv, 2016. [Kor17a] Michael F. Korns. Evolutionary linear discriminant analysis for multiclass classification problems. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '17, pages 233--234, Berlin, Germany, 15-19 July 2017. ACM. [Kor17b] Michael F. Korns. Genetic programming symbolic classification: A study. In Wolfgang Banzhaf, Randal S. Olson, William Tozier, and Rick Riolo, editors, Genetic Programming Theory and Practice XV, Genetic and Evolutionary Computation, pages 39--54, University of Michigan in Ann Arbor, USA, May 18--20 2017. Springer. [Kor21a] Arthur Kordon. Practical issues in human and artificial intelligence interaction. In Bistra Vassileva and Moti Zwilling, editors, Responsible AI and Ethical Issues for Businesses and Governments, chapter 3, pages 35--53. IGI Global, 2021. [Kor21b] Michael Korns. 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The computational theory of intelligence: Applications to genetic programming and turing machines. International Journal of Modern Nonlinear Theory and Application, 4:10--20, March 2015. [Kov15b] Miha Kovacic. Modeling of total decarburization of spring steel with genetic programming. Materials and Manufacturing Processes, 30(4):434--443, 2015. [Kov17] Miha Kovacic. Genetic programming and Store Steel Ltd. In Predrag Cosic, Gordana Baric, Goran Dukic, Tihomir Opetuk, Maja Trstenjak, and Max Regenfelder, editors, 9th International Scientific Conference, Management of Technology - Step to Sustainable Production (MOTSP 2017), Dubrovnik Croatia, April 5-7 2017. Keynote Speaker. [Kow98] Ryszard Kowalczyk. On linguistic approximation with genetic programming. In Jose Mira, Angel Pasqual del Pobil, and Moonis Ali, editors, Methodology and Tools in Knowledge-Based Systems, volume 1415 of LNCS, pages 200--224, Benicassim, Castelln, Sapin, June 1998. Springer Verlag. [Koy20] Anil Koyuncu. Boosting Automated Program Repair for Adoption By Practitioners. PhD thesis, Interdisciplinary Centre for Security, Reliability and Trust (SNT), University of Luxembourg, Luxembourg, 7 December 2020. [Koz89] J. R. Koza. Hierarchical genetic algorithms operating on populations of computer programs. In N. S. Sridharan, editor, Proceedings of the Eleventh International Joint Conference on Artificial Intelligence IJCAI-89, volume 1, pages 768--774, Detroit, MI, USA, 20-25 August 1989. Morgan Kaufmann. [Koz90a] J. Koza. Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems. Technical Report STAN-CS-90-1314, Dept. of Computer Science, Stanford University, June 1990. [Koz90b] John R. Koza. A genetic approach to econometric modeling. In Sixth World Congress of the Econometric Society, Barcelona, Spain, 1990. [Koz90c] John R. Koza. Genetically breeding populations of computer programs to solve problems in artificial intelligence. In Proceedings of the Second International Conference on Tools for AI, pages 819--827, Herndon, Virginia, USA, 6-9 November 1990. IEEE Computer Society Press, Los Alamitos, CA, USA. [Koz90d] John R. Koza. Integrating symbolic processing into genetic algorithms. In Workshop on Integrating Symbolic and Neural Processes at AAAI-90, Boston, 29 July 1990. AAAI. [Koz90e] John R. Koza. Non-linear genetic algorithms for solving problems. United States Patent 4935877, 19 June 1990. filed may 20, 1988, issued june 19, 1990, 4,935,877. Australian patent 611,350 issued september 21, 1991. Canadian patent 1,311,561 issued december 15, 1992. [Koz91a] John R. Koza. Concept formation and decision tree induction using the genetic programming paradigm. In H.-P. Schwefel and R. M"anner, editors, Parallel Problem Solving from Nature - Proceedings of 1st Workshop, PPSN 1, volume 496 of Lecture Notes in Computer Science, pages 124--128, Dortmund, Germany, 1-3 October 1991. Springer-Verlag. [Koz91b] John R. Koza. Evolution and co-evolution of computer programs to control independent-acting agents. In Jean-Arcady Meyer and Stewart W. Wilson, editors, From Animals to Animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior, pages 366--375, Paris, France, 24-28 September 1991. MIT Press. [Koz91c] John R. Koza. Evolving a computer program to generate random numbers using the genetic programming paradigm. In Richard K. Belew and Lashon B. Booker, editors, Proceedings of the Fourth International Conference on Genetic Algorithms, pages 37--44, University of California - San Diego, La Jolla, CA, USA, 13-16 July 1991. Morgan Kaufmann. [Koz91d] John R. Koza. A genetic approach to econometric modeling. In Paul Bourgine and Bernard Walliser, editors, Economics and Cognitive Science, pages 57--75. Pergamon Press, Oxford, UK, 1991. [Koz91e] John R. Koza. Genetic evolution and co-evolution of computer programs. In Christopher Taylor Charles Langton, J. Doyne Farmer, and Steen Rasmussen, editors, Artificial Life II, volume X of SFI Studies in the Sciences of Complexity, pages 603--629. Addison-Wesley, Santa Fe Institute, New Mexico, USA, February 1990 1991. [Koz91f] John R. Koza. A hierarchical approach to learning the boolean multiplexer function. In Gregory J. E. Rawlins, editor, Foundations of genetic algorithms, pages 171--192. Morgan Kaufmann, Indiana University, USA, 15-18 July 1991. [Koz92a] J. R. Koza. Evolution of subsumption using genetic programming. In F. J. Varela and P. Bourgine, editors, Proceedings of the First European Conference on Artificial Life. Towards a Practice of Autonomous Systems, pages 110--119, Paris, France, 11-13 December 1992. MIT Press. [Koz92b] John R. Koza. A genetic approach to finding a controller to back up a tractor-trailer truck. In Proceedings of the 1992 American Control Conference, volume III, pages 2307--2311, Evanston, IL, USA, 1992. American Automatic Control Council. [Koz92c] John R. Koza. A genetic approach to the truck backer upper problem and the inter-twined spiral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, volume IV, pages 310--318, Baltimore, USA, 7-11 June 1992. IEEE Press. [Koz92d] John R. Koza. Genetic evolution and co-evolution of game strategies. In Sylvain Sorin, editor, The International Conference on Game Theory and Its Applications, Stony Brook, New York, July 15 1992. [Koz92e] John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992. [Koz92f] John R. Koza. The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems. In Branko Soucek and the IRIS Group, editors, Dynamic, Genetic, and Chaotic Programming, pages 203--321. John Wiley, New York, 1992. [Koz92g] John R. Koza. Hierarchical automatic function definition in genetic programming. In L. Darrell Whitley, editor, Foundations of Genetic Algorithms 2, pages 297--318, Vail, Colorado, USA, 24--29 July 1992. Morgan Kaufmann. [Koz92h] John R. Koza. Non-linear genetic algorithms for solving problems by finding a fit composition of functions. United States Patent 5136686, 4 August 1992. filed march 28, 1990, issued august 4, 1992. 5,136,686. [Koz93a] John R. Koza, editor. Artificial Life at Stanford 1993. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, 1993. [Koz93b] John R. Koza. Discovery of a main program and reusable subroutines using genetic programming. In Proceedings of the Fifth Workshop on Neural Networks: An International Conference on Computational Intelligence: Neural Networks, Fuzzy Systems, Evolutionary Programming, and Virtual Reality, pages 109--118, 1993. [Koz93c] John R. Koza. Discovery of rewrite rules in Lindenmayer systems and state transition rules in cellular automata via genetic programming. In Symposium on Pattern Formation (SPF-93), Claremont, California, USA, February 1993. [Koz93d] John R. Koza, editor. Genetic Algorithms at Stanford 1993. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, 1993. [Koz93e] John R. Koza. Simultaneous discovery of detectors and a way of using the detectors via genetic programming. In 1993 IEEE International Conference on Neural Networks, volume III, pages 1794--1801, San Francisco, USA, 1993. IEEE. [Koz93f] John R. Koza. Simultaneous discovery of reusable detectors and subroutines using genetic programming. In Stephanie Forrest, editor, Proceedings of the 5th International Conference on Genetic Algorithms, ICGA-93, pages 295--302, University of Illinois at Urbana-Champaign, 17-21 July 1993. Morgan Kaufmann. [Koz94a] J. R. Koza. Recognizing patterns in protein sequences using iteration-performing calculations in genetic programming. In 1994 IEEE World Congress on Computational Intelligence, volume 1, pages 244--249, Orlando, Florida, USA, 27-29 June 1994. IEEE Press. [Koz94b] John R. Koza. Architecture-altering operations for evolving the architecture of a multipart program in genetic programming. Technical Report STAN-CS-94-1528, Dept. of Computer Science, Stanford University, Stanford, California 94305, USA, October 1994. [Koz94c] John R. Koza, editor. Artificial Life at Stanford 1994. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, June 1994. [Koz94d] John R. Koza. Automated discovery of detectors and iteration-performing calculations to recognize patterns in protein sequences using genetic programming. In Proceedings of the Conference on Computer Vision and Pattern Recognition, pages 684--689. IEEE Computer Society Press, 1994. [Koz94e] John R. Koza. Evolution of a computer program for classifying protein segments as transmembrane domains using genetic programming. In Russ Altman, Douglas Brutlag, Peter Karp, Richard Lathrop, and David Searls, editors, Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pages 244--252. AAAI Press, 1994. [Koz94f] John R. Koza. Evolution of a subsumption architecture that performs a wall following task for an autonomous mobile robot via genetic programming. In Stephen Jose Hanson, Thomas Petsche, Ronald L. Rivest, and Michael Kearns, editors, Computational Learning Theory and Natural Learning Systems, volume 2, pages 321--346. MIT Press, Cambridge, MA, USA, June 1994. [Koz94g] John R. Koza. Evolution of emergent cooperative behavior using genetic programming. In Ray Paton, editor, Computing with Biological Metaphors, pages 280--297. Chapman & Hall, London, UK, 1994. [Koz94h] John R. Koza, editor. Genetic Algorithms at Stanford 1994. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, December 1994. [Koz94i] John R. Koza. Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4(2):87--112, June 1994. [Koz94j] John R. Koza. Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge Massachusetts, May 1994. [Koz94k] John R. Koza. Genetic Programming II Videotape: The next generation. MIT Press, 55 Hayward Street, Cambridge, MA, USA, 1994. [Koz94l] John R. Koza. Introduction to genetic programming. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 2, pages 21--42. MIT Press, Cambridge, MA, USA, 1994. [Koz94m] John R. Koza. Scalable learning in genetic programming using automatic function definition. In Kenneth E. Kinnear, Jr., editor, Advances in Genetic Programming, chapter 5, pages 99--117. MIT Press, Cambridge, MA, USA, 1994. [Koz94n] John R. Koza. Spontaneous emergence of self-replicating and evolutionarily self-improving computer programs. In Christopher G. Langton, editor, Artificial Life III, volume XVII of SFI Studies in the Sciences of Complexity, pages 225--262. Addison-Wesley, Santa Fe, New Mexico, USA, 15-19 June 1992 1994. [Koz95a] John Koza. Genetic programming for economic modelling. In Suran Goonatilake and Philip Treleaven, editors, Intelligent Systems for Finance and Business, chapter 14, pages 251--269. John Wiley & Sons, 605 Third Avenue, New York, NY 10158-0012, USA, 1995. [Koz95b] John R. Koza. Evolving the architecture of a multi-part program in genetic programming using architecture-altering operations. In John Robert McDonnell, Robert G. Reynolds, and David B. Fogel, editors, Evolutionary Programming IV Proceedings of the Fourth Annual Conference on Evolutionary Programming, pages 695--717, San Diego, CA, USA, 1-3 March 1995. MIT Press. [Koz95c] John R. Koza. Gene duplication to enable genetic programming to concurrently evolve both the architecture and work-performing steps of a computer program. In C. Raymond Perrault, Chris S. Mellish, Renato deMori, Tony Cohn, Gordon McCalla, and Ramasamy Uthurusamy, editors, IJCAI-95 Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, volume 1, pages 734--740, Montreal, Quebec, Canada, 20-25 August 1995. Morgan Kaufmann. [Koz95d] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 1995. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, December 1995. [Koz95e] John R. Koza. A response to the ml-95 paper entitled ``Hill climbing beats genetic search on a Boolean circuit synthesis of Koza's''. Distributed 11 July 1995 at the 1995 International Machine Learning Conference in Tahoe City, California, USA, 11 July 1995. [Koz95f] John R. Koza. Survey of genetic algorithms and genetic programming. In Proceedings of 1995 WESCON Conference, pages 589--594, San Francisco, CA, USA, 709 November 1995. IEEE. [Koz95g] John R. Koza. Two ways of discovering the size and shape of a computer program to solve a problem. In Larry J. Eshelman, editor, Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95), pages 287--294, Pittsburgh, PA, USA, 15-19 July 1995. Morgan Kaufmann. [Koz96] John R. Koza, editor. Late Breaking Papers at the Genetic Programming 1996 Conference Stanford University July 28-31, 1996, Stanford University, CA, USA, 28--31 July 1996. Stanford Bookstore. [Koz97a] John R. Koza. Classifying protein segments as transmembrane domains using genetic programming and architecture-altering operations. In Thomas Baeck, David B. Fogel, and Zbigniew Michalewicz, editors, Handbook of Evolutionary Computation, chapter section G6.1, pages G6.1:1--5. Oxford University Press, 1997. [Koz97b] John R. Koza. Future work and practical applications of genetic programming. In Thomas Baeck, David B. Fogel, and Zbigniew Michalewicz, editors, Handbook of Evolutionary Computation, chapter section H1.1, pages H1.1--1--6. Oxford University Press, 1997. [Koz97c] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 1997. Stanford Bookstore, Stanford, California, 94305-3079 USA, 17 March 1997. [Koz97d] John R. Koza. Genetic programming: Automatic programming of computers. EvoNews, 1(3):4--7, March 1997. [Koz97e] John R. Koza, editor. Late Breaking Papers at the 1997 Genetic Programming Conference, Stanford University, CA, USA, 13--16 July 1997. Stanford Bookstore. [Koz98a] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 1998. Stanford Bookstore, Stanford, California, 94305-3079 USA, 1998. [Koz98b] John R. Koza. Genetic programming. In James G. Williams and Allen Kent, editors, Encyclopedia of Computer Science and Technology, volume 39, pages 29--43. Marcel-Dekker, 1998. Supplement 24. [Koz98c] John R. Koza, editor. Late Breaking Papers at the 1998 Genetic Programming Conference, University of Wisconsin, Madison, WI, USA, 22-25 July 1998. Omni Press. [Koz98d] John R. Koza. Using biology to solve a problem in automated machine learning. In Clive D. L. Wynne and John E. R. Staddon, editors, Models of Action: Mechanisms for Adaptive Behavior, chapter 5, pages 157--199. Lawrence Erlbaum Associates, Hillsdale, NJ, USA, 1998. [Koz99a] John Koza. What is genetic programming? www, 25 October 1999. [Koz99b] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 1999. Stanford Bookstore, Stanford, California, 94305-3079 USA, 1999. [Koz99c] John R. Koza. Human-competitive machine intelligence by means of genetic algorithms. In Lashon Booker, Stephanie Forrest, Melanie Mitchell, and Rick Riolo, editors, Festschrift in honor of John H. Holland, pages 15--22, Ann Arbor, MI: Center for the Study of Complex Systems, May 15 - 18 1999. [Koz00a] John R. Koza. Cs 426 / mis 226 / ee 392k: Genetic algorithms and genetic programming winter 2000 course reader. Technical Report 2810000021091, Stanford University Bookstore, Stanford, CA, USA, 2000. [Koz00b] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 2000. Stanford University Bookstore, Stanford, California, 94305-3079 USA, Phone 415-329-1217 or 800-533-2670, December 2000. [Koz00c] John R. Koza. Human-competitive machine intelligence by means of genetic programming. IEEE Intelligent Systems, 15(3):76--78, May-June 2000. [Koz02a] John R. Koza. Automatic synthesis of both the topology and numerical parameters for complex structures using genetic programming. In Amaresh Chakrabarti, editor, Engineering Design Synthesis, pages 319--337. Springer, 2002. [Koz02b] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 2002. Stanford University Bookstore, Stanford, California, 94305-3079 USA, June 2002. [Koz03a] John R. Koza. Automatic synthesis of topologies and numerical parameters. In Fred Glover and Gary A. Kochenberger, editors, Handbook of Metaheuristics, number 57 in International Series in Operations Research & Management Science, chapter 4, pages 83--104. Kluwer Academic Publishers, 2003. [Koz03b] John R. Koza, editor. Genetic Algorithms and Genetic Programming at Stanford 2003. Stanford University Bookstore, Stanford, California, 94305-3079 USA, June 2003. [Koz03c] John R. Koza. Human-competitive applications of genetic programming. In Ashish Ghosh and Shigeyeoshi Tsutsui, editors, Advances in Evolutionary Computing: Theory and Applications, pages 663--682. Springer, Berlin, 2003. [Koz03d] John R. Koza. Routine high-return human-competitive machine learning. In M. Arif Wani, K. Cois, and K. Hafeez, editors, Proceedings of the International Conference on Machine Learning and Applications, pages 6--12, Los Angeles, 2003. [Koz04] J. R. Koza. The role of hierarchies and reuse in genetic programming in achieving scalable automatic programming. In R. Poli, S. Cagnoni, M. Keijzer, E. Costa, F. Pereira, G. Raidl, S. C. Upton, D. Goldberg, H. Lipson, E. de Jong, J. Koza, H. Suzuki, H. Sawai, I. Parmee, M. Pelikan, K. Sastry, D. Thierens, W. Stolzmann, P. L. Lanzi, S. W. Wilson, M. O'Neill, C. Ryan, T. Yu, J. F. Miller, I. Garibay, G. Holifield, A. S. Wu, T. Riopka, M. M. Meysenburg, A. W. Wright, N. Richter, J. H. Moore, M. D. Ritchie, L. Davis, R. Roy, and M. Jakiela, editors, GECCO 2004 Workshop Proceedings, Seattle, Washington, USA, 26-30 June 2004. [Koz05] John Koza. Routine human-competitive machine intelligence by means of genetic programming. In Heng-Da Cheng, editor, Proceedings of the 8th Joint Conference in Information Systems (JCIS 2005), Salt Lake City, USA, 21-25 July 2005. [Koz08a] John R. Koza. Human-competitive machine invention by means of genetic programming. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 22(3):185--193, August 2008. [Koz08b] John R. Koza. Introduction to genetic programming: tutorial. In Marc Ebner, Mike Cattolico, Jano van Hemert, Steven Gustafson, Laurence D. Merkle, Frank W. Moore, Clare Bates Congdon, Christopher D. Clack, Frank W. Moore, William Rand, Sevan G. Ficici, Rick Riolo, Jaume Bacardit, Ester Bernado-Mansilla, Martin V. Butz, Stephen L. Smith, Stefano Cagnoni, Mark Hauschild, Martin Pelikan, and Kumara Sastry, editors, GECCO-2008 tutorials, pages 2299--2338, Atlanta, GA, USA, 12-16 July 2008. ACM. [Koz09] John Koza. Automated design using Darwinian evolution and genetic programming. 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In Peter van der Putten and Maarten van Someren, editors, The Benelearn 1999 Competition, page 3.5, Sociaal-Wetenschappelijke Informatica, Universiteit van Amsterdam, 2 November 1999. [Lan99c] W. B. Langdon. Linear increase in tree height leads to sub-quadratic bloat. In Thomas Haynes, William B. Langdon, Una-May O'Reilly, Riccardo Poli, and Justinian Rosca, editors, Foundations of Genetic Programming, pages 55--56, Orlando, Florida, USA, 13 July 1999. [Lan99d] W. B. Langdon. Scaling of program tree fitness spaces. Evolutionary Computation, 7(4):399--428, Winter 1999. [Lan99e] W. B. Langdon. Size fair and homologous tree crossovers. Technical Report SEN-R9907, Centrum voor Wiskunde en Informatica, CWI, P.O. Box 94079, Kruislaan 413, NL-1090 GB Amsterdam, The Netherlands, 11 April 1999. [Lan99f] W. B. Langdon. Size fair and homologous tree genetic programming crossovers. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 2, pages 1092--1097, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [Lan99g] W. B. Langdon. Size fair tree crossovers. In Eric Postma and Marc Gyssen, editors, Proceedings of the Eleventh Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'99), pages 255--256, Kasteel Vaeshartelt, Maastricht, Holland, 3-4 November 1999. [Lan99h] Pier Luca Lanzi. Extending the representation of classifier conditions part I: From binary to messy coding. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 337--344, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [Lan00a] W. B. Langdon. Gecco'99 student workshop. Robotica, 18(1):87, January 2000. [Lan00b] W. B. Langdon. Quadratic bloat in genetic programming. In Darrell Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), pages 451--458, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann. [Lan00c] William B. Langdon. Genetic programming and evolvable machines: Books and other resources. Genetic Programming and Evolvable Machines, 1(1/2):165--169, April 2000. [Lan00d] William B. Langdon. Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines, 1(1/2):95--119, April 2000. [Lan01a] W. B. Langdon. Evolutionary data fusion. Technical Report RN/01/19, University College, London, UK, 3 April 2001. [Lan01b] W. B. Langdon. Long random linear programs do not generalize. Genetic Programming and Evolvable Machines, 2(2):95--100, June 2001. [Lan01c] W. B. Langdon. Prediction. Technical report, Commercial, 2001. [Lan02a] W. B. Langdon. Application of genetic programming in drug lead discovery. 8th Iberoamerican Conference on Artificial Intelligence, 12 November 2002. Invited conference speaker. [Lan02b] W. B. Langdon. Characteristics of the genetic programming search space. Technical Report 364, University of Hertfordshire, Computer Science, University of Hertfordshire, 2 February 2002. [Lan02c] W. B. Langdon. Convergence rates for the distribution of program outputs. In W. B. Langdon, E. Cant'u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 812--819, New York, 9-13 July 2002. Morgan Kaufmann Publishers. [Lan02d] W. B. Langdon. How many good programs are there? How long are they? In Kenneth A. De Jong, Riccardo Poli, and Jonathan E. Rowe, editors, Foundations of Genetic Algorithms VII, pages 183--202, Torremolinos, Spain, 4-6 September 2002. Morgan Kaufmann. Published 2003. [Lan02e] W. B. Langdon. Random search is parsimonious. In Erick Cant'u-Paz, editor, Late Breaking Papers at the Genetic and Evolutionary Computation Conference (GECCO-2002), pages 308--315, New York, NY, 9-13 July 2002. AAAI. [Lan02f] W. B. Langdon. Size of random programs to ensure uniformity. In Hendrik Blockeel and Marc Denecker, editors, Proceedings of the Fourteenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC'02), pages 459--460, Leuven, Belgium, 21-22 October 2002. [Lan02g] William B. Langdon. Evolutionary computation II session at BNAIC 2002. Newsletter BNVKI, 19(6):145--146, December 2002. [Lan02h] William B. Langdon. A hybrid genetic programming neural network classifier for use in drug discovery. In Ajith Abraham, Javier Ruiz-del-Solar, and Mario K"oppen, editors, Soft Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications Vol. 87, page 6, Universidad de Chile, Chile, 1-4 December 2002. IOS Press Amsterdam, Berlin, Oxford, Tokyo, Washington D.C. Invited conference speaker. [Lan02i] William B. Langdon. Structure of the genetic programming search space. Report 330, Dagstuhl, Germany, 13-18 January 2002. [Lan02j] William B. Langdon. Was Occam wrong? Blunting Occam's razor. BNVKI newsletter, 19(3):56--57, June 2002. [Lan03a] W. B. Langdon. Convergence of program fitness landscapes. In E. Cant'u-Paz, J. A. Foster, K. Deb, D. Davis, R. Roy, U.-M. O'Reilly, H.-G. Beyer, R. Standish, G. Kendall, S. Wilson, M. Harman, J. Wegener, D. Dasgupta, M. A. Potter, A. C. Schultz, K. Dowsland, N. Jonoska, and J. Miller, editors, Genetic and Evolutionary Computation -- GECCO-2003, volume 2724 of LNCS, pages 1702--1714, Chicago, 12-16 July 2003. Springer-Verlag. [Lan03b] W. B. Langdon. The distribution of reversible functions is Normal. In Rick L. Riolo and Bill Worzel, editors, Genetic Programming Theory and Practice, chapter 11, pages 173--187. Kluwer, 2003. [Lan03c] W. B. Langdon. Predicting cancer. UCL Science, 17:2, September 2003. [Lan03d] Pier Luca Lanzi. XCS with stack-based genetic programming. In Ruhul Sarker, Robert Reynolds, Hussein Abbass, Kay Chen Tan, Bob McKay, Daryl Essam, and Tom Gedeon, editors, Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pages 1186--1191, Canberra, 8-12 December 2003. IEEE Press. [Lan04a] W. B. Langdon. Global distributed evolution of l-systems fractals. In Maarten Keijzer, Una-May O'Reilly, Simon M. Lucas, Ernesto Costa, and Terence Soule, editors, Genetic Programming, Proceedings of EuroGP'2004, volume 3003 of LNCS, pages 349--358, Coimbra, Portugal, 5-7 April 2004. Springer-Verlag. [Lan04b] W. B. Langdon. Lindenmayer systems fractals evolved by Pfeiffer 10 September -- 9 November 2003. Research Note RN/04/13, University College, London, 19 July 2004. [Lan05a] W. B. Langdon. The distribution of amorphous computer outputs. In Susan Stepney and Stephen Emmott, editors, The Grand Challenge in Non-Classical Computation: International Workshop, York, UK, 18-19 April 2005. [Lan05b] W. B. Langdon. Evolving benchmarks. In Katja Verbeeck, Karl Tuyls, Ann Nowe, Bernard Manderick, and Bart Kuijpers, editors, Proceedings of the Seventeenth Belgium/Netherlands Conference on Artificial Intelligence (BNAIC 2005), pages 365--366, Koninklijke Vlaamse Academie van Belgie voor Wetenschappen en Kunsten, Brussel, Belgium, 17-18 October 2005. Royal Flemish Academy of Belgium for Science and Arts, KVAB. [Lan05c] W. B. Langdon. Pfeiffer -- a distributed open-ended evolutionary system. In Bruce Edmonds, Nigel Gilbert, Steven Gustafson, David Hales, and Natalio Krasnogor, editors, AISB'05: Proceedings of the Joint Symposium on Socially Inspired Computing (METAS 2005), pages 7--13, University of Hertfordshire, Hatfield, UK, 12-15 April 2005. SSAISB 2005 Convention. [Lan06a] W. B. Langdon. The halting probability in von Neumann architectures. Technical Report CSM-456, Computer Science, University of Essex, UK, July 2006. [Lan06b] W. B. Langdon. Mapping non-conventional extensions of genetic programming. In Cristian S. Calude, Michael J. Dinneen, Gheorghe Paun, Grzegorz Rozenberg, and Susan Stepney, editors, Unconventional Computing 2006, volume 4135 of LNCS, pages 166--180, York, 4-8 September 2006. Springer-Verlag. [Lan06c] W. B. Langdon. Predicting ten thousand bits from ten thousand inputs. Technical Report CSM-457, Department of Computer Science, University of Essex, Colchester, UK, 10 August 2006. [Lan06d] W. B. Langdon. Web usage of the GP bibliography. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 1(4):16--21, December 2006. [Lan07a] W. B. Langdon. A SIMD interpreter for genetic programming on GPU graphics cards. Technical Report CSM-470, Department of Computer Science, University of Essex, Colchester, UK, 3 July 2007. [Lan07b] Pier Luca Lanzi. An analysis of generalization in xcs with symbolic conditions. In Dipti Srinivasan and Lipo Wang, editors, 2007 IEEE Congress on Evolutionary Computation, pages 2149--2156, Singapore, 25-28 September 2007. IEEE Computational Intelligence Society, IEEE Press. [Lan08a] W. B. Langdon. Evolving GeneChip correlation predictors on parallel graphics hardware. In Jun Wang, editor, 2008 IEEE World Congress on Computational Intelligence, pages 4152--4157, Hong Kong, 1-6 June 2008. IEEE Computational Intelligence Society, IEEE Press. [Lan08b] W. B. Langdon. A fast high quality pseudo random number generator for graphics processing units. In Jun Wang, editor, 2008 IEEE World Congress on Computational Intelligence, pages 459--465, Hong Kong, 1-6 June 2008. IEEE. [Lan08c] W. B. Langdon. A field guide to genetic programing, April 2008. [Lan08d] W. B. Langdon. A field guide to genetic programming, March 2008. Thank you to Seminar 08051. [Lan08e] W. B. Langdon. Genetic programming for drug discovery. Technical Report CES-481, Computing and Electronic Systems, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK, 26 February 2008. [Lan08f] W. B. Langdon. Is this the future of academic publishing? SIGEVOlution, 3(1):16, Spring 2008. [Lan09a] W. B. Langdon. A CUDA SIMT interpreter for genetic programming. Technical Report TR-09-05, Department of Computer Science, King's College London, Strand, WC2R 2LS, UK, 18 June 2009. Revised. [Lan09b] W. B. Langdon. Scaling of program functionality. Genetic Programming and Evolvable Machines, 10(1):5--36, March 2009. [Lan10a] W. B. Langdon. 2-bit flip mutation elementary fitness landscapes. Research Note RN/10/04, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 15 September 2010. Presented at Dagstuhl Seminar 10361, Theory of Evolutionary Algorithms, 8 September 2010. [Lan10b] W. B. Langdon. Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In Francisco Fernandez de Vega and Erick Cantu-Paz, editors, Parallel and Distributed Computational Intelligence, volume 269 of Studies in Computational Intelligence, chapter 5, pages 113--141. Springer, January 2010. [Lan10c] W. B. Langdon. A many threaded CUDA interpreter for genetic programming. In Anna Isabel Esparcia-Alcazar, Aniko Ekart, Sara Silva, Stephen Dignum, and A. Sima Uyar, editors, Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010, volume 6021 of LNCS, pages 146--158, Istanbul, 7-9 April 2010. Springer. [Lan11a] W. B. Langdon. Elementary bit string mutation landscapes. In Hans-Georg Beyer and W. B. Langdon, editors, Foundations of Genetic Algorithms, pages 25--41, Schwarzenberg, Austria, 5-9 January 2011. ACM. [Lan11b] W. B. Langdon. Graphics processing units and genetic programming: An overview. Soft Computing, 15:1657--1669, August 2011. [Lan11c] W. B. Langdon. Minimising testing in genetic programming. Technical Report RN/11/10, Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 11 April 2011. [Lan11d] William B. Langdon. Generalisation in genetic programming. In Natalio Krasnogor, Pier Luca Lanzi, Andries Engelbrecht, David Pelta, Carlos Gershenson, Giovanni Squillero, Alex Freitas, Marylyn Ritchie, Mike Preuss, Christian Gagne, Yew Soon Ong, Guenther Raidl, Marcus Gallager, Jose Lozano, Carlos Coello-Coello, Dario Landa Silva, Nikolaus Hansen, Silja Meyer-Nieberg, Jim Smith, Gus Eiben, Ester Bernado-Mansilla, Will Browne, Lee Spector, Tina Yu, Jeff Clune, Greg Hornby, Man-Leung Wong, Pierre Collet, Steve Gustafson, Jean-Paul Watson, Moshe Sipper, Simon Poulding, Gabriela Ochoa, Marc Schoenauer, Carsten Witt, and Anne Auger, editors, GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, page 205, Dublin, Ireland, 12-16 July 2011. ACM. [Lan12a] W. B. Langdon. Distilling GeneChips with genetic programming on the Emerald GPU supercomputer. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 6(1):15--21, 25 July 2012. [Lan12b] W. B. Langdon. Genetic improvement of programs. In Radomil Matousek, editor, 18th International Conference on Soft Computing, MENDEL 2012, Brno, Czech Republic, 27-29 June 2012. Brno University of Technology. Invited keynote. [Lan12c] W. B. Langdon. The halting problem in the clear light of probability. Communications of the ACM, 55(6):6, June 2012. letters to the editor. [Lan12d] W. B. Langdon. Initial experiences of the emerald: e-infrastructure south GPU supercomputer. Research Note RN/12/08, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 17 June 2012. [Lan13a] W. B. Langdon. Which is faster: Bowtie2GP > Bowtie > Bowtie2 > BWA. Research Note RN/13/03, Department of Computer Science, University College London, Gower Street, London WC1E 6BT, UK, 21 January 2013. [Lan13b] W. B. Langdon. Which is faster: Bowtie2GP > Bowtie > Bowtie2 > BWA. In Francisco Luna, editor, GECCO 2013 Late breaking abstracts workshop, pages 1741--1742, Amsterdam, The Netherlands, 6-10 July 2013. ACM. [Lan13c] William B. Langdon. Large scale bioinformatics data mining with parallel genetic programming on graphics processing units. In Shigeyoshi Tsutsui and Pierre Collet, editors, Massively Parallel Evolutionary Computation on GPGPUs, Natural Computing Series, chapter 15, pages 311--347. Springer, 2013. [Lan14a] W. B. Langdon. Improved CUDA 3D medical image registration, 15 December 2014. [Lan14b] William B. Langdon. Genetic improvement of programs. In Franz Winkler, Viorel Negru, Tetsuo Ida, Tudor Jebelean, Dana Petcu, Stephen Watt, and Daniela Zaharie, editors, 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC 2014), pages 14--19, Timisoara, 22-25 September 2014. IEEE. Keynote. [Lan14c] William B. Langdon. News of the GP bibliography http://www.cs.bham.ac.uk/ wbl/biblio/. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 6(3-4):12--16, July 2014. [Lan15a] W. B. Langdon. Genetic improvement of software for multiple objectives. In Yvan Labiche and Marcio Barros, editors, SSBSE, volume 9275 of LNCS, pages 12--28, Bergamo, Italy, September 5-7 2015. Springer. Invited keynote. [Lan15b] W. B. Langdon. Performance of genetic programming optimised Bowtie2 on genome comparison and analytic testing (GCAT) benchmarks. BioData Mining, 8(1), 8 January 2015. [Lan15c] William B. Langdon. Genetically improved software. In Amir H. Gandomi, Amir H. Alavi, and Conor Ryan, editors, Handbook of Genetic Programming Applications, chapter 8, pages 181--220. Springer, 2015. [Lan16a] W. B. Langdon. Benchmarking BarraCUDA on epigenetic DNA and nVidia Pascal GPUs. Technical Report RN/16/10, University College, London, London, UK, 17 December 2016. Also available as BIORXIV/2016/095075. [Lan16b] William B. Langdon. The genetic improvement fitness landscape. Technical Report RN/16/04, University College, London, London, UK, 10 June 2016. [Lan16c] William B. Langdon. Kin selection with twin genetic programming. In Julia Handl, Emma Hart, Peter R. Lewis, Manuel Lopez-Ibanez, Gabriela Ochoa, and Ben Paechter, editors, 14th International Conference on Parallel Problem Solving from Nature, volume 9921 of LNCS, pages 313--323, Edinburgh, 17-21 September 2016. Springer. [Lan17a] W. B. Langdon. Evolving better RNAfold C source code. Technical Report RN/17/08, University College, London, London, UK, 2017. Also available as bioRxiv preprint 201640. [Lan17b] W. B. Langdon. Long-term evolution of genetic programming populations. Technical Report RN/17/05, University College, London, London, UK, 24 March 2017. Also available as arXiv 1843365. [Lan17c] William B. Langdon. Convergence in genetic programming. Technical Report 5, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 7-12 May 2017. 3.17. [Lan17d] William B. Langdon. Landscape of the Triangle program. Technical Report 5, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 7-12 May 2017. 3.18. [Lan17e] William B. Langdon. Long-term evolution of genetic programming populations. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '17, pages 235--236, Berlin, 15-19 July 2017. ACM. [Lan17f] William B. Langdon. Long-term stability of genetic programming landscapes. In Nadarajen Veerapen, Fabio Daolio, Arnaud Liefooghe, Sebastien Verel, and Gabriela Ochoa, editors, GECCO 2017 Workshop on Landscape-Aware Heuristic Search, Berlin, 2017. No paper, presentation only. [Lan18a] W. B. Langdon. Evolving square root into binary logarithm. Technical Report RN/18/05, University College, London, London, UK, 27 June 2018. [Lan18b] W. B. Langdon. Genetic improvement GISMOE blue software tool demo. Technical Report RN/18/06, University College, London, London, UK, 22 September 2018. [Lan18c] W. B. Langdon. Human-competitive awards 2018. SIGEVOlution, 11(4):3--8, December 2018. [Lan18d] W. B. Langdon. Human-competitive awards 2018. Technical Report RN/18/07, University College, London, London, UK, 22 October 2018. in SIGEVOlution 2018(4). [Lan19a] Bill Langdon. Mark Harman wins both IEEE and ACM awards in 2019. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 12(2):10--11, August 2019. [Lan19b] W. B. Langdon. Big data driven genetic improvement for maintenance of legacy software systems. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 12(3):6--9, 2019. [Lan19c] W. B. Langdon. Genetic improvement of data gives double precision invsqrt. In Brad Alexander, Saemundur O. Haraldsson, Markus Wagner, and John R. Woodward, editors, 7th edition of GI @ GECCO 2019, pages 1709--1714, Prague, Czech Republic, July 13-17 2019. ACM. [Lan19d] W. B. Langdon. Home monitoring for Parkinson's patients already... Communications of the ACM, 62(2):7, February 2019. Letter to the editor. [Lan19e] W. B. Langdon. Parallel GPQUICK. In Carola Doerr, editor, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 63--64, Prague, Czech Republic, July 13-17 2019. ACM. [Lan19f] W. B. Langdon. Software improvement by data improvement. IEEE Software Blog, 19 February 2019. [Lan20a] W. B. Langdon. Genetic improvement of genetic programming. In Alexander (Sandy) Brownlee, Saemundur O. Haraldsson, Justyna Petke, and John R. Woodward, editors, GI @ CEC 2020 Special Session, page id24061, Internet, 19-24 July 2020. IEEE Computational Intelligence Society, IEEE Press. [Lan20b] W. B. Langdon. Genetic programming and evolvable machines at 20. Genetic Programming and Evolvable Machines, 21(1-2):205--217, June 2020. Twentieth Anniversary Issue. [Lan20c] W. B. Langdon. Multi-threaded memory efficient crossover in C++ for generational genetic programming. ArXiv, 22 September 2020. [Lan20d] W. B. Langdon. Multi-threaded memory efficient crossover in C++ for generational genetic programming. SIGEVOLution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 13(3):2--4, October 2020. [Lan20e] William B. Langdon. Fast generation of big random binary trees. Technical Report RN/20/01, Computer Science, University College, London, Gower Street, London, UK, 13 January 2020. [Lan21a] William B. Langdon. Fitness first. In Wolfgang Banzhaf, Leonardo Trujillo, Stephan Winkler, and Bill Worzel, editors, Genetic Programming Theory and Practice XVIII, Genetic and Evolutionary Computation, pages 143--164, East Lansing, MI, USA, 19-21 May 2021. Springer. [Lan21b] William B. Langdon. Fitness first and fatherless crossover. In Francisco Chicano, Alberto Tonda, Krzysztof Krawiec, Marde Helbig, Christopher W. Cleghorn, Dennis G. Wilson, Georgios Yannakakis, Luis Paquete, Gabriela Ochoa, Jaume Bacardit, Christian Gagne, Sanaz Mostaghim, Laetitia Jourdan, Oliver Schuetze, Petr Posik, Carlos Segura, Renato Tinos, Carlos Cotta, Malcolm Heywood, Mengjie Zhang, Leonardo Trujillo, Risto Miikkulainen, Bing Xue, Aneta Neumann, Richard Allmendinger, Fuyuki Ishikawa, Inmaculada Medina-Bulo, Frank Neumann, and Andrew M. Sutton, editors, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '21, pages 253--254, Internet, 10-14 July 2021. Association for Computing Machinery. [Lan21c] William B. Langdon. Incremental evaluation in genetic programming. In Ting Hu, Nuno Lourenco, and Eric Medvet, editors, EuroGP 2021: Proceedings of the 24th European Conference on Genetic Programming, volume 12691 of LNCS, pages 229--246, Virtual Event, 7-9 April 2021. Springer Verlag. [Lan22a] W. B. Langdon. Evolving open complexity. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 15(1), March 2022. [Lan22b] W. B. Langdon. Genetic programming convergence. Genetic Programming and Evolvable Machines, 23(1):71--104, March 2022. [Lan22c] W. B. Langdon. Open to evolve embodied intelligence. In Fumiya Iida, Josie Hughes, Arsen Abdulali, and Ryman Hashem, editors, Proceedings of 2022 International Conference on Embodied Intelligence, EI-2022, volume 1292 of IOP Conference Series: Materials Science and Engineering, page 012021, Internet, Cambridge, 23-25 March 2022. IOP Publishing. [Lan22d] W. B. Langdon. A trillion genetic programming instructions per second. ArXiv:2205.03251, 6 May 2022. [Lan22e] William B. Langdon. Deep genetic programming trees are robust. ACM Transactions on Evolutionary Learning and Optimization, 2(2), June 2022. [Lan22f] William B. Langdon. Dissipative arithmetic. Complex Systems, 31(3):287--309, 2022. [Lan22g] William B. Langdon. Failed disruption propagation in integer genetic programming. In Heike Trautmann, Carola Doerr, Alberto Moraglio, Thomas Bartz-Beielstein, Bogdan Filipic, Marcus Gallagher, Yew-Soon Ong, Abhishek Gupta, Anna V Kononova, Hao Wang, Michael Emmerich, Peter A. N. Bosman, Daniela Zaharie, Fabio Caraffini, Johann Dreo, Anne Auger, Konstantin Dietric, Paul Dufosse, Tobias Glasmachers, Nikolaus Hansen, Olaf Mersmann, Petr Posik, Tea Tusar, Dimo Brockhoff, Tome Eftimov, Pascal Kerschke, Boris Naujoks, Mike Preuss, Vanessa Volz, Bilel Derbel, Ke Li, Xiaodong Li, Saul Zapotecas, Qingfu Zhang, Mark Coletti, Catherine (Katie) Schuman, Eric Scott, Robert Patton, Paul Wiegand, Jeffrey K. Bassett, Chathika Gunaratne, Tinkle Chugh, Richard Allmendinger, Jussi Hakanen, Daniel Tauritz, John Woodward, Manuel Lopez-Ibanez, John McCall, Jaume Bacardit, Alexander Brownlee, Stefano Cagnoni, Giovanni Iacca, David Walker, Jamal Toutouh, UnaMay O'Reilly, Penousal Machado, Joao Correia, Sergio Nesmachnow, Josu Ceberio, Rafael Villanueva, Ignacio Hidalgo, Francisco Fernandez de Vega, Giuseppe Paolo, Alex Coninx, Antoine Cully, Adam Gaier, Stefan Wagner, Michael Affenzeller, Bobby R. Bruce, Vesna Nowack, Aymeric Blot, Emily Winter, William B. Langdon, Justyna Petke, Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stuetzle, David Paetzel, Alexander Wagner, Michael Heider, Nadarajen Veerapen, Katherine Malan, Arnaud Liefooghe, Sebastien Verel, Gabriela Ochoa, Mohammad Nabi Omidvar, Yuan Sun, Ernesto Tarantino, De Falco Ivanoe, Antonio Della Cioppa, Scafuri Umberto, John Rieffel, Jean-Baptiste Mouret, Stephane Doncieux, Stefanos Nikolaidis, Julian Togelius, Matthew C. Fontaine, Serban Georgescu, Francisco Chicano, Darrell Whitley, Oleksandr Kyriienko, Denny Dahl, Ofer Shir, Lee Spector, Alma Rahat, Richard Everson, Jonathan Fieldsend, Handing Wang, Yaochu Jin, Erik Hemberg, Marwa A. Elsayed, Michael Kommenda, William La Cava, Gabriel Kronberger, and Steven Gustafson, editors, Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '22, pages 574--577, Boston, USA, 9-13 July 2022. Association for Computing Machinery. [Lan22h] William B. Langdon. Genetic programming convergence [hot of the press]. In Marcus Gallagher, editor, GECCO 2022 Hot off the Press, GECCO '22, pages 27--28, Boston, USA, 9-13 July 2022. Association for Computing Machinery. [Lan22i] Michele Lanotte. Soft computing approach for predicting the effects of waste rubber-bitumen interaction phenomena on the viscosity of rubberized bitumen. Sustainability, 14(21):Article No. 13798, 2022. [Lan23a] W. B. Langdon. Jaws 30. Genetic Programming and Evolvable Machines, 24(2):Article number: 19, December 2023. Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection. [Lan23b] W. B. Langdon. Response to comments on ``Jaws 30''. Genetic Programming and Evolvable Machines, 24(2):Article number: 26, December 2023. Special Issue: Thirtieth Anniversary of Genetic Programming: On the Programming of Computers by Means of Natural Selection. [Lan24a] David James Landaeta. Automated feature extraction using genetic programming. USA Patent, 30 April 2024. US 11,972,842 B2. [Lan24b] W. B. Langdon. Generating trust. Communications of the ACM, 67(6):8, June 2024. Letter to the editor. [Lan24c] W. B. Langdon. ``lost gems'' icga 1985: Proceedings of an international conference on genetic algorithms and their applications. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 17(3), September 2024. [Lan24d] W. B. Langdon. Searching the genetic programming bibliography. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 17(1):Article No: 2, March 2024. [Lan24e] William B. Langdon. Evolutionary robustness, 14 July 2024. Invited talk. [Lan25a] William B. Langdon. A genetic improvement parameter benchmark: rand_malloc.c. In Emma Hart, Tomas Horvath, Zhiyuan Tan, and Sarah Thomson, editors, 24th UK Workshop on Computational Intelligence (UKCI 2025), volume 1468 of Advances in Intelligent Systems and Computing, pages 127--132, Edinburgh Napier University, 3--5 September 2025. Springer. [Lan25b] William B. Langdon. Improving a parallel C++ Intel AVX-512 SIMD linear genetic programming interpreter. ArXiv, 9 December 2025. [Lan25c] William B. Langdon. Open-ended evolution with linear genetic programming. In Nicola Catenacci Volpi, Christoph Salge, Daniel Polani, Gianluca Baldassarre, Vieri Giuliano Santucci, Cedric Colas, Kenzo Clauw, Erik Lintunen, Bente Riegler, and Jan Kim, editors, The 7th International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2025), University of Hertfordshire, 8-10 September 2025. [Lan26a] W. B. Langdon. Who found what: Searches of the genetic programming bibliography. SIGEVOlution newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation, 19(2), June 2026. forthcoming. [Lan26b] William B. Langdon. Long term evolution experiments with linear genetic programming. In Wolfgang Banzhaf and Ting Hu, editors, Recent Advances in Linear Genetic Programming, chapter 4, pages 53--84. Springer, 2026. forthcoming. [LAP+18] Nuno Lourenco, Filipe Assuncao, Francisco B. Pereira, Ernesto Costa, and Penousal Machado. Structured grammatical evolution: A dynamic approach. In Conor Ryan, Michael O'Neill, and J. J. 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In Conor Ryan, Michael O'Neill, and J. J. Collins, editors, Handbook of Grammatical Evolution, chapter 14, pages 341--366. Springer, 2018. [Lov03a] Thomas Loveard. Genetic Programming for Classification Learning Problems. PhD thesis, Department of Computer Science, Royal Melbourne Institute of Technology, RMIT, Australia, 20 January 2003. [Lov03b] Thomas Loveard. Genetic programming with meta-search: Searching for a successful population within the classification domain. In Conor Ryan, Terence Soule, Maarten Keijzer, Edward Tsang, Riccardo Poli, and Ernesto Costa, editors, Genetic Programming, Proceedings of EuroGP'2003, volume 2610 of LNCS, pages 119--129, Essex, 14-16 April 2003. Springer-Verlag. [Lov08] Joern Loviscach. Graphical control of a parametric equalizer. In AES, Amsterdam, 17-20 May 2008. [Low99] David Lowsky. Using a cooperative fitness function to coevolve optimal strategies in the iterated prisoner's dilemma game. In John R. Koza, editor, Genetic Algorithms and Genetic Programming at Stanford 1999, pages 131--139. Stanford Bookstore, Stanford, California, 94305-3079 USA, 15 March 1999. [LP97a] W. B. Langdon and R. Poli. An analysis of the MAX problem in genetic programming. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 222--230, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann. [LP97b] W. B. Langdon and R. Poli. Fitness causes bloat. Technical Report CSRP-97-09, University of Birmingham, School of Computer Science, Birmingham, B15 2TT, UK, 24 February 1997. [LP97c] W. B. Langdon and R. Poli. Fitness causes bloat. In P. K. Chawdhry, R. Roy, and R. K. Pant, editors, Soft Computing in Engineering Design and Manufacturing, pages 13--22. Springer-Verlag London, 23-27 June 1997. [LP97d] W. B. Langdon and R. Poli. Fitness causes bloat: Mutation. In John Koza, editor, Late Breaking Papers at the GP-97 Conference, pages 132--140, Stanford, CA, USA, 13-16 July 1997. Stanford Bookstore. [LP97e] W. B. Langdon and R. Poli. Fitness causes bloat: Mutation. In Chris Clack, Kanta Vekaria, and Nadav Zin, editors, ET'97 Theory and Application of Evolutionary Computation, pages 59--77, University College London, UK, 15 December 1997. [LP97f] W. B. Langdon and R. Poli. Genetic programming bloat with dynamic fitness. Technical Report CSRP-97-29, University of Birmingham, School of Computer Science, 3 December 1997. [LP97g] W. B. Langdon and R. Poli. Genetic programming in europe. Report of the EvoGP Working Group on Genetic Programming of the European Network of Excellence in Evolutionary Computing, 30 November 1997. [LP97h] W. B. Langdon and R. Poli. Price's theorem and the MAX problem. Technical Report CSRP-97-4, University of Birmingham, School of Computer Science, January 1997. [LP98a] W. B. Langdon and R. Poli. Better trained ants for genetic programming. Technical Report CSRP-98-12, University of Birmingham, School of Computer Science, April 1998. [LP98b] W. B. Langdon and R. Poli. Boolean functions fitness spaces. In John R. Koza, editor, Late Breaking Papers at the Genetic Programming 1998 Conference, pages 125--133, University of Wisconsin, Madison, Wisconsin, USA, 22-25 July 1998. Stanford University Bookstore. [LP98c] W. B. Langdon and R. Poli. Evogp report summary. EvoNews, (6):6, Winter 1998. [LP98d] W. B. Langdon and R. Poli. Fitness causes bloat: Mutation. In Wolfgang Banzhaf, Riccardo Poli, Marc Schoenauer, and Terence C. Fogarty, editors, Proceedings of the First European Workshop on Genetic Programming, volume 1391 of LNCS, pages 37--48, Paris, 14-15 April 1998. Springer-Verlag. [LP98e] W. B. Langdon and R. Poli. Genetic programming bloat with dynamic fitness. In Wolfgang Banzhaf, Riccardo Poli, Marc Schoenauer, and Terence C. Fogarty, editors, Proceedings of the First European Workshop on Genetic Programming, volume 1391 of LNCS, pages 97--112, Paris, 14-15 April 1998. Springer-Verlag. [LP98f] W. B. Langdon and R. Poli. Why ants are hard. Technical Report CSRP-98-4, University of Birmingham, School of Computer Science, January 1998. Presented at GP-98. [LP98g] W. B. Langdon and R. Poli. Why ants are hard. In John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors, Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 193--201, University of Wisconsin, Madison, Wisconsin, USA, 22-25 July 1998. Morgan Kaufmann. [LP98h] W. B. Langdon and R. Poli. Why ``building blocks'' don't work on parity problems. Technical Report CSRP-98-17, University of Birmingham, School of Computer Science, 13 July 1998. [LP99a] W. B. Langdon and R. Poli. Boolean functions fitness spaces. In Riccardo Poli, Peter Nordin, William B. Langdon, and Terence C. Fogarty, editors, Genetic Programming, Proceedings of EuroGP'99, volume 1598 of LNCS, pages 1--14, Goteborg, Sweden, 26-27 May 1999. Springer-Verlag. [LP99b] Pier Luca Lanzi and Alessandro Perrucci. Extending the representation of classifier conditions Part II: from messy coding to S-expressions. In Wolfgang Banzhaf, Jason Daida, Agoston E. Eiben, Max H. Garzon, Vasant Honavar, Mark Jakiela, and Robert E. Smith, editors, Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 345--352, Orlando, Florida, USA, 13-17 July 1999. Morgan Kaufmann. [LP00] Hod Lipson and Jordan B. Pollack. Automatic design and manufacture of robotic lifeforms. Nature, (406):974--978, 31 August 2000. [LP01] Sean Luke and Liviu Panait. A survey and comparison of tree generation algorithms. In Lee Spector, Erik D. Goodman, Annie Wu, W. B. Langdon, Hans-Michael Voigt, Mitsuo Gen, Sandip Sen, Marco Dorigo, Shahram Pezeshk, Max H. Garzon, and Edmund Burke, editors, Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2001), pages 81--88, San Francisco, California, USA, 7-11 July 2001. Morgan Kaufmann. [LP02a] W. B. Langdon and Riccardo Poli. Foundations of Genetic Programming. Springer-Verlag, 2002. [LP02b] Sean Luke and Liviu Panait. Fighting bloat with nonparametric parsimony pressure. In Juan J. Merelo-Guervos, Panagiotis Adamidis, Hans-Georg Beyer, Jose-Luis Fernandez-Villacanas, and Hans-Paul Schwefel, editors, Parallel Problem Solving from Nature - PPSN VII, number 2439 in Lecture Notes in Computer Science, LNCS, pages 411--421, Granada, Spain, 7-11 September 2002. Springer-Verlag. [LP02c] Sean Luke and Liviu Panait. Is the perfect the enemy of the good? In W. B. Langdon, E. Cant'u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 820--828, New York, 9-13 July 2002. Morgan Kaufmann Publishers. [LP02d] Sean Luke and Liviu Panait. Lexicographic parsimony pressure. In W. B. Langdon, E. Cant'u-Paz, K. Mathias, R. Roy, D. Davis, R. Poli, K. Balakrishnan, V. Honavar, G. Rudolph, J. Wegener, L. Bull, M. A. Potter, A. C. Schultz, J. F. Miller, E. Burke, and N. Jonoska, editors, GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, pages 829--836, New York, 9-13 July 2002. Morgan Kaufmann Publishers. [LP05a] W. B. Langdon and R. Poli. On Turing complete T7 and MISC F-4 program fitness landscapes. Technical Report CSM-445, Computer Science, University of Essex, UK, December 2005. [LP05b] W. B. Langdon and Riccardo Poli. Evolutionary solo pong players. Technical Report CSM-423, Department of Computer Science, University of Essex, Colchester, UK, 17 March 2005. [LP05c] William B. Langdon and Riccardo Poli. Evolutionary solo pong players. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 3, pages 2621--2628, Edinburgh, UK, 2-5 September 2005. IEEE Press. [LP05d] William B. Langdon and Riccardo Poli. Evolving problems to learn about particle swarm and other optimisers. In David Corne, Zbigniew Michalewicz, Marco Dorigo, Gusz Eiben, David Fogel, Carlos Fonseca, Garrison Greenwood, Tan Kay Chen, Guenther Raidl, Ali Zalzala, Simon Lucas, Ben Paechter, Jennifier Willies, Juan J. Merelo Guervos, Eugene Eberbach, Bob McKay, Alastair Channon, Ashutosh Tiwari, L. Gwenn Volkert, Dan Ashlock, and Marc Schoenauer, editors, Proceedings of the 2005 IEEE Congress on Evolutionary Computation, volume 1, pages 81--88, Edinburgh, UK, 2-5 September 2005. IEEE Press. [LP06a] W. B. Langdon and R. Poli. Evolving problems to learn about particle swarm optimisers and other search algorithms. Technical Report CSM-455, Computer Science, University of Essex, UK, June 2006. [LP06b] W. B. Langdon and R. Poli. The halting probability in von Neumann architectures. In Pierre Collet, Marco Tomassini, Marc Ebner, Steven Gustafson, and Anik'o Ek'art, editors, Proceedings of the 9th European Conference on Genetic Programming, volume 3905 of Lecture Notes in Computer Science, pages 225--237, Budapest, Hungary, 10 - 12 April 2006. Springer. [LP06c] William B. Langdon and Riccardo Poli. Finding social landscapes for psos via kernels. In Gary G. Yen, Lipo Wang, Piero Bonissone, and Simon M. Lucas, editors, Proceedings of the 2006 IEEE Congress on Evolutionary Computation, pages 6118--6125, Vancouver, 16-21 July 2006. IEEE Press. [LP06d] William B. Langdon and Riccardo Poli. On Turing complete t7 and misc f--4 program fitness landscapes. In Dirk V. Arnold, Thomas Jansen, Michael D. Vose, and Jonathan E. Rowe, editors, Theory of Evolutionary Algorithms, number 06061 in Dagstuhl Seminar Proceedings, Dagstuhl, Germany, 5-10 February 2006. Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany. [date of citation: 2006-01-01]. [LP06e] Sean Luke and Liviu Panait. A comparison of bloat control methods for genetic programming. Evolutionary Computation, 14(3):309--344, Fall 2006. [LP07a] W. B. Langdon and Riccardo Poli. Evolving problems to learn about particle swarm optimisers and other search algorithms. IEEE Transactions on Evolutionary Computation, 11(5):561--578, October 2007. [LP07b] Martin H. Luerssen and David M. W. Powers. Evolvability and redundancy in shared grammar evolution. In Dipti Srinivasan and Lipo Wang, editors, 2007 IEEE Congress on Evolutionary Computation, pages 370--377, Singapore, 25-28 September 2007. IEEE Computational Intelligence Society, IEEE Press. [LP07c] Martin H. Luerssen and David M. W. Powers. Graph design by graph grammar evolution. In Dipti Srinivasan and Lipo Wang, editors, 2007 IEEE Congress on Evolutionary Computation, pages 386--393, Singapore, 25-28 September 2007. IEEE Computational Intelligence Society, IEEE Press. [LP08a] William B. Langdon and Riccardo Poli. Mapping non-conventional extensions of genetic programming. Natural Computing, 7:21--43, March 2008. Invited contribution to special issue on Unconventional computing. [LP08b] Martin H. Luerssen and David M. W. Powers. Evolving encapsulated programs as shared grammars. Genetic Programming and Evolvable Machines, 9(3):203--228, September 2008. [LP13] Linxia Liao and Radu Pavel. Machinery time to failure prediction - case study and lesson learned for a spindle bearing application. In IEEE Conference on Prognostics and Health Management (PHM 2013), 24-27 June 2013. [LP15] William B. Langdon and Justyna Petke. Software is not fragile. In Pierre Parrend, Paul Bourgine, and Pierre Collet, editors, Complex Systems Digital Campus E-conference, CS-DC'15, Proceedings in Complexity, pages 203--211. Springer, September 30-October 1 2015. Invited talk. [LP16] William B. Langdon and Justyna Petke. Genetic improvement. IEEE Software Blog, February 3 2016. [LP18] William B. Langdon and Justyna Petke. Evolving better software parameters. In Thelma Elita Colanzi and Phil McMinn, editors, SSBSE 2018 Hot off the Press Track, volume 11036 of LNCS, pages 363--369, Montpellier, France, 8-9 September 2018. Springer. [LP19a] W. B. Langdon and Justyna Petke. Genetic improvement of data gives binary logarithm from sqrt. In Richard Allmendinger, Carlos Cotta, Carola Doerr, Pietro S. Oliveto, Thomas Weise, Ales Zamuda, Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Tea Tusar, Konstantinos Varelas, David Camacho-Fernandez, Massimiliano Vasile, Annalisa Riccardi, Bilel Derbel, Ke Li, Xiaodong Li, Saul Zapotecas, Qingfu Zhang, Ozgur Akman, Khulood Alyahya, Juergen Branke, Jonathan Fieldsend, Tinkle Chugh, Jussi Hakanen, Josu Ceberio Uribe, Valentino Santucci, Marco Baioletti, John McCall, Emma Hart, Daniel R. Tauritz, John R. Woodward, Koichi Nakayama, Chika Oshima, Stefan Wagner, Michael Affenzeller, Eneko Osaba, Javier Del Ser, Pascal Kerschke, Boris Naujoks, Vanessa Volz, Anna I Esparcia-Alcazar, Riyad Alshammari, Erik Hemberg, Tokunbo Makanju, Brad Alexander, Saemundur O. Haraldsson, Markus Wagner, Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stuetzle, David Walker, Matt Johns, Nick Ross, Ed Keedwell, Masaya Nakata, Anthony Stein, Takato Tatsumi, Nadarajen Veerapen, Arnaud Liefooghe, Sebastien Verel, Gabriela Ochoa, Stephen Smith, Stefano Cagnoni, Robert M. Patton, William La Cava, Randal Olson, Patryk Orzechowski, Ryan Urbanowicz, Akira Oyama, Koji Shimoyama, Hemant Kumar Singh, Kazuhisa Chiba, Pramudita Satria Palar, Alma Rahat, Richard Everson, Handing Wang, Yaochu Jin, Marcus Gallagher, Mike Preuss, Olivier Teytaud, Fernando Lezama, Joao Soares, and Zita Vale, editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 413--414, Prague, Czech Republic, 13-17 July 2019. ACM. [LP19b] Ricardo H. R. Lima and Aurora T. R. Pozo. Evolving convolutional neural networks through grammatical evolution. In Richard Allmendinger, Carlos Cotta, Carola Doerr, Pietro S. Oliveto, Thomas Weise, Ales Zamuda, Anne Auger, Dimo Brockhoff, Nikolaus Hansen, Tea Tusar, Konstantinos Varelas, David Camacho-Fernandez, Massimiliano Vasile, Annalisa Riccardi, Bilel Derbel, Ke Li, Xiaodong Li, Saul Zapotecas, Qingfu Zhang, Ozgur Akman, Khulood Alyahya, Juergen Branke, Jonathan Fieldsend, Tinkle Chugh, Jussi Hakanen, Josu Ceberio Uribe, Valentino Santucci, Marco Baioletti, John McCall, Emma Hart, Daniel R. Tauritz, John R. Woodward, Koichi Nakayama, Chika Oshima, Stefan Wagner, Michael Affenzeller, Eneko Osaba, Javier Del Ser, Pascal Kerschke, Boris Naujoks, Vanessa Volz, Anna I Esparcia-Alcazar, Riyad Alshammari, Erik Hemberg, Tokunbo Makanju, Brad Alexander, Saemundur O. Haraldsson, Markus Wagner, Silvino Fernandez Alzueta, Pablo Valledor Pellicer, Thomas Stuetzle, David Walker, Matt Johns, Nick Ross, Ed Keedwell, Masaya Nakata, Anthony Stein, Takato Tatsumi, Nadarajen Veerapen, Arnaud Liefooghe, Sebastien Verel, Gabriela Ochoa, Stephen Smith, Stefano Cagnoni, Robert M. Patton, William La Cava, Randal Olson, Patryk Orzechowski, Ryan Urbanowicz, Akira Oyama, Koji Shimoyama, Hemant Kumar Singh, Kazuhisa Chiba, Pramudita Satria Palar, Alma Rahat, Richard Everson, Handing Wang, Yaochu Jin, Marcus Gallagher, Mike Preuss, Olivier Teytaud, Fernando Lezama, Joao Soares, and Zita Vale, editors, GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pages 179--180, Prague, Czech Republic, 13-17 July 2019. ACM. [LP26] Shruti Lall and Nelishia Pillay. Genetic programming for energy-efficient device-edge collaborative inference. In 29th International Conference, EvoApplications 2026, Toulouse, 8-10 April 2026. Springer. [LPB08] W. B. Langdon, R. Poli, and W. Banzhaf. An eigen analysis of the GP community. Genetic Programming and Evolvable Machines, 9(3):171--182, September 2008. [LPB16] William B. Langdon, Justyna Petke, and Bobby R. Bruce. Optimising quantisation noise in energy measurement. In Julia Handl, Emma Hart, Peter R. Lewis, Manuel Lopez-Ibanez, Gabriela Ochoa, and Ben Paechter, editors, 14th International Conference on Parallel Problem Solving from Nature, volume 9921 of LNCS, pages 249--259, Edinburgh, 17-21 September 2016. Springer. [LPBC23a] William B. Langdon, Justyna Petke, Aymeric Blot, and David Clark. Genetically improved software with fewer data caches misses. In Sara Silva, Luis Paquete, Leonardo Vanneschi, Nuno Lourenco, Ales Zamuda, Ahmed Kheiri, Arnaud Liefooghe, Bing Xue, Ying Bi, Nelishia Pillay, Irene Moser, Arthur Guijt, Jessica Catarino, Pablo Garcia-Sanchez, Leonardo Trujillo, Carla Silva, and Nadarajen Veerapen, editors, Proceedings of the Companion Conference on Genetic and Evolutionary Computation, GECCO '23, pages 799--802, Lisbon, Portugal, 15-19 July 2023. Association for Computing Machinery. [LPBC23b] William B. Langdon, Justyna Petke, Aymeric Blot, and David Clark. GI software with fewer data caches misses. ArXiv, 6 April 2023. [LPC95] Jean Louchet, Xavier Provot, and David Crochemore. Evolutionary identification of cloth animation models. In Dimitri Terzolpoulos and Daniel Thalmann, editors, Computer Animation and Simulation '95, LNCS, pages 44--54, Maastricht, Netherlands, 2-3 September 1995. Springer-Verlag. Proceedings of the Eurographics Workshop. [LPC12] Nuno Lourenco, Francisco Pereira, and Ernesto Costa. Evolving evolutionary algorithms. In Gisele L. Pappa, John Woodward, Matthew R. Hyde, and Jerry Swan, editors, GECCO 2012 2nd Workshop on Evolutionary Computation for the Automated Design of Algorithms, pages 51--58, Philadelphia, Pennsylvania, USA, 7-11 July 2012. ACM. [LPC13a] Nuno Lourenco, Francisco B. Pereira, and Ernesto Costa. Learning selection strategies for evolutionary algorithms. In Pierrick Legrand, Marc-Michel Corsini, Jin-Kao Hao, Nicolas Monmarche, Evelyne Lutton, and Marc Schoenauer, editors, Artificial Evolution, EA 2013, volume 8752 of Lecture Notes in Computer Science, pages 197--208, Bordeaux, France, 21-23 October 2013. Springer. [LPC13b] Nuno Lourenco, Francisco Baptista Pereira, and Ernesto Costa. The importance of the learning conditions in hyper-heuristics. In Christian Blum, Enrique Alba, Anne Auger, Jaume Bacardit, Josh Bongard, Juergen Branke, Nicolas Bredeche, Dimo Brockhoff, Francisco Chicano, Alan Dorin, Rene Doursat, Aniko Ekart, Tobias Friedrich, Mario Giacobini, Mark Harman, Hitoshi Iba, Christian Igel, Thomas Jansen, Tim Kovacs, Taras Kowaliw, Manuel Lopez-Ibanez, Jose A. Lozano, Gabriel Luque, John McCall, Alberto Moraglio, Alison Motsinger-Reif, Frank Neumann, Gabriela Ochoa, Gustavo Olague, Yew-Soon Ong, Michael E. Palmer, Gisele Lobo Pappa, Konstantinos E. Parsopoulos, Thomas Schmickl, Stephen L. Smith, Christine Solnon, Thomas Stuetzle, El-Ghazali Talbi, Daniel Tauritz, and Leonardo Vanneschi, editors, GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, pages 1525--1532, Amsterdam, The Netherlands, 6-10 July 2013. ACM. [LPC15] Nuno Lourenco, Francisco B. Pereira, and Ernesto Costa. SGE: A structured representation for grammatical evolution. In Stephane Bonnevay, Pierrick Legrand, Nicolas Monmarche, Evelyne Lutton, and Marc Schoenauer, editors, Artificial Evolution, volume 9554 of LNCS, pages 136--148, Lyon, France, 26-28 October 2015. Springer. [LPC16] Nuno Lourenco, Francisco B. Pereira, and Ernesto Costa. Unveiling the properties of structured grammatical evolution. Genetic Programming and Evolvable Machines, 17(3):251--289, September 2016. [LPC21] William B. Langdon, Justyna Petke, and David Clark. Dissipative polynomials. In Nadarajen Veerapen, Katherine Malan, Arnaud Liefooghe, Sebastien Verel, and Gabriela Ochoa, editors, 5th Workshop on Landscape-Aware Heuristic Search, GECCO 2021 Companion, pages 1683--1691, Internet, 10-14 July 2021. ACM. [LPC25] William B. Langdon, Justyna Petke, and David Clark. gem5/z3/gcc/clang/redis glibc heap fitness landscapes. In Antonio M. Mora, Anna I. Esparcia-Alcazar, and Maria Sofia Cruz, editors, Evostar 2025 Late breaking abstracts, pages 27--32, Trieste, 23-25 April 2025. [LPD+18] Hongyan Li, Yuzhong Peng, Chuyan Deng, Yonghua Pan, Daoqing Gong, and Hao Zhang. Multicellular gene expression programming-based hybrid model for precipitation prediction coupled with EMD. In De-Shuang Huang, Vitoantonio Bevilacqua, Prashan Premaratne, and Phalguni Gupta, editors, Intelligent Computing Theories and Application - 14th International Conference, ICIC 2018, Wuhan, China, August 15-18, 2018, Proceedings, Part I, volume 10954 of Lecture Notes in Computer Science, pages 207--218. Springer, 2018. [LPD+19] Hongyan Li, Yuzhong Peng, Chuyan Deng, Yonghua Pan, Daoqing Gong, and Hao Zhang. A hybrid precipitation prediction method based on multicellular gene expression programming. arXiv, 2019. [LPdV18] Jose Maria Luna, Mykola Pechenizkiy, Maria Jose del Jesus, and Sebastian Ventura. Mining context-aware association rules using grammar-based genetic programming. IEEE Transactions on Cybernetics, 48(11):3030--3044, November 2018. [LPDV20] Jose Maria Luna, Mykola Pechenizkiy, Wouter Duivesteijn, and Sebastian Ventura. Exceptional in so many ways--discovering descriptors that display exceptional behavior on contrasting scenarios. IEEE Access, 8:200982--200994, 30 October 2020. [LPHK05] W. B. Langdon, Riccardo Poli, Owen Holland, and Thiemo Krink. Understanding particle swarm optimisation by evolving problem landscapes. In Luca Maria Gambardella, Payman Arabshahi, and Alcherio Martinoli, editors, Proceedings SIS 2005 IEEE Swarm Intelligence, pages 30--37, Pasadena, California, USA, 8-10 June 2005. IEEE. [LPI21] Lushen Liao, Adam Kotaro Pindur, and Hitoshi Iba. Genetic programming with random binary decomposition for multi-class classification problems. In Yew-Soon Ong, editor, 2021 IEEE Congress on Evolutionary Computation (CEC), pages 564--571, Krakow, Poland, 28 June-1 July 2021. [LPL18] William B. Langdon, Justyna Petke, and Ronny Lorenz. Evolving better RNAfold structure prediction. In Mauro Castelli, Lukas Sekanina, and Mengjie Zhang, editors, EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming, volume 10781 of LNCS, pages 220--236, Parma, Italy, 4-6 April 2018. Springer Verlag. [LPMK08] William B. Langdon, Riccardo Poli, Nicholas F. McPhee, and John R. Koza. Genetic programming: An introduction and tutorial, with a survey of techniques and applications. In John Fulcher and Lakhmi C. Jain, editors, Computational Intelligence: A Compendium, volume 115 of Studies in Computational Intelligence (SCI), chapter 22, pages 927--1028. Springer-Verlag, 2008. [LPMS20] Ricardo Henrique Remes Lima, Aurora Pozo, Alexander Mendiburu, and Roberto Santana. A symmetric grammar approach for designing segmentation models. In Yaochu Jin, editor, 2020 IEEE Congress on Evolutionary Computation, CEC 2020, page paper id24460, internet, 19-24 July 2020. IEEE Computational Intelligence Society, IEEE Press. [LPNF99] W. B. 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Can investors benefit from using trading rules evolved by genetic programming? a test of the adaptive efficiency of u.s. stock markets with margin trading allowed. In Herbert Dawid and Willi Semmler, editors, Computational Methods in Economic Dynamics, volume 13 of Dynamic Modeling and Econometrics in Economics and Finance, pages 77--108. Springer, 2011. [MS11b] Milos Minarik and Lukas Sekanina. Evolution of iterative formulas using cartesian genetic programming. In Andreas K"onig, Andreas Dengel, Knut Hinkelmann, Koichi Kise, Robert J. Howlett, and Lakhmi C. Jain, editors, Proceedings of the 15th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011) Part I, volume 6881 of Lecture Notes in Computer Science, pages 11--20, Kaiserslautern, Germany, September 12-14 2011. Springer. [MS11c] Alberto Moraglio and Sara Silva. Geometric nelder-mead algorithm on the space of genetic programs. In Natalio Krasnogor, Pier Luca Lanzi, Andries Engelbrecht, David Pelta, Carlos Gershenson, Giovanni Squillero, Alex Freitas, Marylyn Ritchie, Mike Preuss, Christian Gagne, Yew Soon Ong, Guenther Raidl, Marcus Gallager, Jose Lozano, Carlos Coello-Coello, Dario Landa Silva, Nikolaus Hansen, Silja Meyer-Nieberg, Jim Smith, Gus Eiben, Ester Bernado-Mansilla, Will Browne, Lee Spector, Tina Yu, Jeff Clune, Greg Hornby, Man-Leung Wong, Pierre Collet, Steve Gustafson, Jean-Paul Watson, Moshe Sipper, Simon Poulding, Gabriela Ochoa, Marc Schoenauer, Carsten Witt, and Anne Auger, editors, GECCO '11: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 1307--1314, Dublin, Ireland, 12-16 July 2011. ACM. [MS12] David J. Murray-Smith. Experimental modelling: system identification, parameter estimation and model optimisation techniques. In David J. Murray-Smith, editor, Modelling and Simulation of Integrated Systems in Engineering, chapter 6, pages 165--214. 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Detecting fraudulent bank transactions using deep learning enhanced with genetic programming. In 18th IEEE International Conference on Intelligent Computer Communication and Processing, ICCP 2022, Cluj-Napoca, Romania, September 22-24, 2022, pages 151--158. IEEE, 2022. [MS23] Jimil Mehta and M. T. Shah. Optimization of bioelectrochemical systems with power of artificial intelligence. In 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC), pages 494--499, October 2023. [MS25a] Eric Medvet and Erica Salvato. The science of soft robots, koichi suzumori, kenjiro fukuda, ryuma niiyama, and kohei nakajima: Isbn 978-9811951732, springer 2023. Genetic Programming and Evolvable Machines, 26:Article no 9, 2025. Book Review. [MS25b] Rui Menoita and Sara Silva. Evolving financial trading strategies with vectorial genetic programming. 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Estimation models generation using linear genetic programming. CLEI Electronic Journal, 13(3):paper 4, December 2009. Regular Issue and Special Issue of Best Papers presented at CLEI 2008, Santa Fe, Argentina. [MSB+22] Manish Motwani, Mauricio Soto, Yuriy Brun, Rene Just, and Claire Le Goues. Quality of automated program repair on real-world defects. IEEE Transactions on Software Engineering, 48(2):637--661, 2022. [MSB+23] Bryan Martins Lima, Naiara Sachetti, Augusto Berndt, Cristina Meinhardt, and Jonata Tyska Carvalho. Adaptive batch size CGP: Improving accuracy and runtime for CGP logic optimization flow. In Gisele Pappa, Mario Giacobini, and Zdenek Vasicek, editors, EuroGP 2023: Proceedings of the 26th European Conference on Genetic Programming, volume 13986 of LNCS, pages 149--164, Brno, Czech Republic, 12-14 April 2023. Springer Verlag. [MSBB25] Claudio Mandrioli, Seung Yeob Shin, Domenico Bianculli, and Lionel Briand. 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