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Model approach to grammatical evolution: theory and case study

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Abstract

Many deficiencies with grammatical evolution (GE) such as inconvenience in solution derivations, modularity analysis, and semantic computing can partly be explained from the angle of genotypic representations. In this paper, we deepen some of our previous work in visualizing concept relationships, individual structures and total evolutionary process, contributing new ideas, perspectives, and methods in these aspects; reveal the principle hidden in early work so that to develop a practical methodology; provide formal proofs for issues of concern which will be helpful for understanding of mathematical essence of issues, establishing of an unified formal framework as well as practical implementation; exploit genotypic modularity like modular discovery systematically which for the lack of supporting mechanism, if not impossible, is done poorly in many existing systems, and finally demonstrate the possible gains through semantic analysis and modular reuse. As shown in this work, the search space and the number of nodes in the parser tree are reduced using concepts from building blocks, and concepts such as the codon-to-grammar mapping and the integer modulo arithmetic used in most existing GE can be abnegated.

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References

  • Aho AV, Lam MS, Sethi R, Ullman JD (2007) Compilers: principles, techniques, and tools, 2nd edn. Pearson Education, New York

  • Alfonseca M, Gil FJS (2013) Evolving an ecology of mathematical expressions with grammatical evolution. BioSystems 111(1):111–119

    Article  Google Scholar 

  • Boolos GS, Burgess JP, Jeffrey RC (2002) Computability and logic, 4th edn. Cambridge University Press, Cambridge

  • Burbidge R, Wilson MS (2014) Vector-valued function estimation by grammatical evolution. Inf Sci 258(1):182–199

  • Castiglione A, Pizzolante R, De Santis A, Carpentieri B, Castiglione A, Palmieri F (2015) Cloud-based adaptive compression and secure management services for 3D healthcare data. Future Gener Comput Syst 43C44(1):120–134

  • D’Apiec C, Nicola CD, Manzo R, Moccia V (2014) Optimal scheduling for aircraft departure. J Ambient Intell Human Comput 5(1):799–807

  • Dempsey I, O’Neill M, Brabazon A (2006) Adaptive trading with grammatical evolution. In: Proceedings of the 2006 IEEE congress on evolutionary computation, vol 1, pp 2587–2592

  • Du X, Ni YC, Xie DT, Yao X, Ye P, Xiao RL (2014) The time complexity analysis of a class of gene expression programming. Soft Comput

  • Esposito C, Ficco M, Palmieri F, Castiglione A (2013) Interconnecting federated clouds by using publish-subscribe service. Cluster Comput 16(4):887–903

    Article  Google Scholar 

  • Fagan D, O’Neill M, Galvan-Lopez E, Brabazon A, McGarraghy S (2010) An analysis of genotype–phenotype maps in grammatical evolution. In: EuroGP 2010, LNCS, vol 6021, pp 62–73

  • Fernandez-Blanco E, Rivero D, Gestal M, Dorado J (2013) Classification of signals by means of genetic programming. Soft Comput 17(1):1929–1937

    Article  Google Scholar 

  • Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129

    MathSciNet  MATH  Google Scholar 

  • France R, Rumpe B (2007) Model-driven development of complex software: a research roadmap. In: Future of software engineering (FOSE2007) in international conference on software engineering (ICSE), vol 1, pp 37–54

  • Gavrilis D, Tsoulos IG, Dermatas E (2008) Selecting and constructing features using grammatical evolution. Pattern Recognit Lett 29(1):1358–1365

    Article  Google Scholar 

  • Habib SJ, Marimuthu PN (2011) Self-organization in ambient networks through molecular assembly. J Ambient Intell Hum Comput 2(3):165–173

    Article  Google Scholar 

  • Harman M, Mansouri SA, Zhang Y (2012) Search-based software engineering: trends, techniques and applications. ACM Comput Surv 45(1):11:1–11:61

  • He P, Kang LS, Fu M (2008) Formality based genetic programming. In: IEEE congress on evolutionary computation

  • He P, Kang LS, Johnson CG, Ying S (2011a) Hoare logic-based genetic programming. Sci China Ser F Inf Sci 54(3):623–637

    Article  MathSciNet  MATH  Google Scholar 

  • He P, Johnson CG, Wang HF (2011b) Modeling grammatical evolution by automaton. Sci China Inf Sci 54(12):2544–2553

    Article  MathSciNet  MATH  Google Scholar 

  • Hopcroft JE, Motwani R, Ullman JD (2008) Automata theory, languages, and computation, 3rd edn. Pearson Education, New York

  • Howard D, Brezulianu A, Kolibal J (2011) Genetic programming of the stochastic interpolation framework: convection diffusion equation. Soft Comput 15(1):71–78

    Article  Google Scholar 

  • Hugosson J, Hemberg E, Brabazon A, O’Neill M (2010) Genotype representation in grammatical evolution. Appl Soft Comput 10(1):36–43

    Article  Google Scholar 

  • Koza JR (1992) Genetic programming. MIT Press, Cambridge

    MATH  Google Scholar 

  • Krawiec K (2014) Genetic programming: where meaning emerges from program code. Genet Program Evolvable Mach 15(1):75–77

    Article  Google Scholar 

  • Langdon WB, Harman M (2015) Optimizing existing software with genetic programming. IEEE Trans Evol Comput 19(1):118–135

    Article  Google Scholar 

  • Li J, Wang Q, Wang C, Cao N, Ren K, Lou WJ (2010) Fuzzy keyword search over encrypted data in cloud computing. In: Proceeding of the 29th IEEE international conference on computer communications (INFOCOM 2010), pp 441–445

  • Li J, Huang XY, Li JW, Chen XF, Xiang Y (2014) Securely outsourcing attribute-based encryption with checkability. IEEE Trans Parallel Distrib Syst 25(8):2201–2210

    Article  Google Scholar 

  • Mckay RI, Hoai NX, Whigham PA, Shan Y, O’Neill M (2010) Grammar-based genetic programming: a survey. Genet Program Evolvable Mach 11(3/4):365–396

    Article  Google Scholar 

  • Mokryani G, Siano P, Piccolo A (2013) Optimal allocation of wind turbines in microgrids by using genetic algorithm. J Ambient Intell Hum Comput 4(1):613–619

    Article  Google Scholar 

  • Montana DJ (1995) Strongly typed genetic programming. Evol Comput 3(2):199–230

    Article  Google Scholar 

  • Oltean M, Grosan C (2003) A comparison of several linear genetic programming techniques. Complex Syst 14(1):285–313

    MathSciNet  MATH  Google Scholar 

  • Oltean M, Grosan C, Diosan L, Mihaila C (2009) Genetic programming with linear representation: a survey. Int J Artif Intell Tools 19(2):197–239

    Article  Google Scholar 

  • O’Neill M, Ryan C (2001) Grammatical evolution. IEEE Trans Evol Comput 5(4):349–358

    Article  Google Scholar 

  • Pierce BC (2002) Types and programming languages. MIT Press, Cambridge

    MATH  Google Scholar 

  • Risco-Martin JL, Colmenar JM, Hidalgo JI, Lanchares J, Diaz J (2014) A methodology to automatically optimize dynamic memory managers applying grammatical evolution. J Syst Softw 91(1):109–123

    Article  Google Scholar 

  • Swafford JM, O’Neill M, Nicolau M, Brabazon A (2011) Exploring grammatical modification with modules in grammatical evolution. In: EuroGP 2011, LNCS, vol 6621, pp 310–321

  • Vanneschi L, Castelli M, Silva S (2014) A survey of semantic methods in genetic programming. Genet Program Evolvable Mach 15(2):195–214

    Article  Google Scholar 

  • Wilson D, Kaur D (2009) Search, neutral evolution, and mapping in evolutionary computing: a case study of grammatical evolution. IEEE Trans Evol Comput 13(3):566–590

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Acknowledgments

The research work was supported by National Natural Science Foundation of China (Grant No. 61170199, 61370117), the Scientific Research Fund of Education Department of Hunan Province, China (Grant No. 11A004),and the Guangzhou Zhujiang Science and Technology Future Fellow Fund (Grant No. 2012J2200094). Besides, He Pei would like to give special thanks to the late Prof. Kang Lishan and Prof. Tang Zhisong for introducing him to the area of evolutionary computation and Formal Methods.

Conflict of interest

There are no conflicts of interest.

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Authors and Affiliations

  1. School of Computer Science and Educational Software, Guangzhou University, Guangzhou, 510006, People’s Republic of China

    Pei He

  2. Key Laboratory of High Confidence Software Technologies (Peking University), Ministry of Education, Beijing, 100871, People’s Republic of China

    Pei He

  3. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, People’s Republic of China

    Pei He & Zelin Deng

  4. Institute of Computational Linguistics, Peking University, Beijing, 100080, People’s Republic of China

    Houfeng Wang

  5. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, People’s Republic of China

    Zhusong Liu

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  1. Pei He
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  2. Zelin Deng
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Correspondence to Pei He.

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Communicated by V. Loia.

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He, P., Deng, Z., Wang, H. et al. Model approach to grammatical evolution: theory and case study. Soft Comput 20, 3537–3548 (2016). https://doi.org/10.1007/s00500-015-1710-9

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