Genetic Programming Bibliography entries for Nathaniel Haut

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GP coauthors/coeditors: Illya Bakurov, Wolfgang Banzhaf, William F Punch, Dirk Colbry, Mark Kotanchek, Kelvin Kotanchek, Yuri Lavinas, Sylvain Cussat-Blanc,

Genetic Programming Articles by Nathaniel Haut

Genetic Programming conference papers by Nathaniel Haut

  1. Yuri Lavinas and Nathaniel Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc. Dynamically Sampling biomedical Images For Genetic Programming. In Ting Hu and Aniko Ekart editors, Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion, pages 515-518, Melbourne, Australia, 2024. Association for Computing Machinery. details

  2. Yuri Lavinas and Nathan Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc. Data Sampling via Active Learning in Cartesian Genetic Programming for Biomedical Data. In Bing Xue editor, 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, 2024. IEEE. details

  3. Yuri Lavinas and Nathaniel Haut and William Punch and Wolfgang Banzhaf and Sylvain Cussat-Blanc. Adaptive Sampling of Biomedical Images with Cartesian Genetic Programming. In Heike Trautmann and Tea Tusar and Penousal Machado and Thomas Baeck editors, 18th International Conference on Parallel Problem Solving from Nature, volume 15148, pages 256-272, University of Applied Sciences Upper Austria, Hagenberg, Austria, 2024. Springer. details

  4. Mark Kotanchek and Nathan Haut and Kelvin Kotanchek. Representation and Reachability: Assumption Impact in Data Modeling. In Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini editors, Genetic Programming Theory and Practice XXI, pages 1-25, University of Michigan, USA, 2024. Springer. details

  5. Illya Bakurov and Nathan Haut and Wolfgang Banzhaf. Sharpness-Aware Minimization in Genetic Programming. In Stephan M. Winkler and Wolfgang Banzhaf and Ting Hu and Alexander Lalejini editors, Genetic Programming Theory and Practice XXI, pages 151-175, University of Michigan, USA, 2024. Springer. details

  6. Nathan Haut and Bill Punch and Wolfgang Banzhaf. Active Learning Informs Symbolic Regression Model Development in Genetic Programming. In Sara Silva and Luis Paquete and Leonardo Vanneschi and Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and Arnaud Liefooghe and Bing Xue and Ying Bi and Nelishia Pillay and Irene Moser and Arthur Guijt and Jessica Catarino and Pablo Garcia-Sanchez and Leonardo Trujillo and Carla Silva and Nadarajen Veerapen editors, Proceedings of the 2023 Genetic and Evolutionary Computation Conference, pages 587-590, Lisbon, Portugal, 2023. Association for Computing Machinery. details

  7. Nathan Haut and Wolfgang Banzhaf and Bill Punch and Dirk Colbry. Accelerating Image Analysis Research with Active Learning Techniques in Genetic Programming. In Stephan Winkler and Leonardo Trujillo and Charles Ofria and Ting Hu editors, Genetic Programming Theory and Practice XX, pages 45-64, Michigan State University, USA, 2023. Springer. details

  8. Nathaniel Haut and Wolfgang Banzhaf and Bill Punch. Active Learning Improves Performance on Regression Tasks inStackGP. In Heike Trautmann and Carola Doerr and Alberto Moraglio and Thomas Bartz-Beielstein and Bogdan Filipic and Marcus Gallagher and Yew-Soon Ong and Abhishek Gupta and Anna V Kononova and Hao Wang and Michael Emmerich and Peter A. N. Bosman and Daniela Zaharie and Fabio Caraffini and Johann Dreo and Anne Auger and Konstantin Dietric and Paul Dufosse and Tobias Glasmachers and Nikolaus Hansen and Olaf Mersmann and Petr Posik and Tea Tusar and Dimo Brockhoff and Tome Eftimov and Pascal Kerschke and Boris Naujoks and Mike Preuss and Vanessa Volz and Bilel Derbel and Ke Li and Xiaodong Li and Saul Zapotecas and Qingfu Zhang and Mark Coletti and Catherine (Katie) Schuman and Eric ``Siggy'' Scott and Robert Patton and Paul Wiegand and Jeffrey K. Bassett and Chathika Gunaratne and Tinkle Chugh and Richard Allmendinger and Jussi Hakanen and Daniel Tauritz and John Woodward and Manuel Lopez-Ibanez and John McCall and Jaume Bacardit and Alexander Brownlee and Stefano Cagnoni and Giovanni Iacca and David Walker and Jamal Toutouh and UnaMay O'Reilly and Penousal Machado and Joao Correia and Sergio Nesmachnow and Josu Ceberio and Rafael Villanueva and Ignacio Hidalgo and Francisco Fernandez de Vega and Giuseppe Paolo and Alex Coninx and Antoine Cully and Adam Gaier and Stefan Wagner and Michael Affenzeller and Bobby R. Bruce and Vesna Nowack and Aymeric Blot and Emily Winter and William B. Langdon and Justyna Petke and Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle and David Paetzel and Alexander Wagner and Michael Heider and Nadarajen Veerapen and Katherine Malan and Arnaud Liefooghe and Sebastien Verel and Gabriela Ochoa and Mohammad Nabi Omidvar and Yuan Sun and Ernesto Tarantino and De Falco Ivanoe and Antonio Della Cioppa and Scafuri Umberto and John Rieffel and Jean-Baptiste Mouret and Stephane Doncieux and Stefanos Nikolaidis and Julian Togelius and Matthew C. Fontaine and Serban Georgescu and Francisco Chicano and Darrell Whitley and Oleksandr Kyriienko and Denny Dahl and Ofer Shir and Lee Spector and Alma Rahat and Richard Everson and Jonathan Fieldsend and Handing Wang and Yaochu Jin and Erik Hemberg and Marwa A. Elsayed and Michael Kommenda and William La Cava and Gabriel Kronberger and Steven Gustafson editors, Proceedings of the 2022 Genetic and Evolutionary Computation Conference Companion, pages 550-553, Boston, USA, 2022. Association for Computing Machinery. details

  9. Nathan Haut and Wolfgang Banzhaf and Bill Punch. Correlation versus RMSE Loss Functions in Symbolic Regression Tasks. In Leonardo Trujillo and Stephan M. Winkler and Sara Silva and Wolfgang Banzhaf editors, Genetic Programming Theory and Practice XIX, pages 31-55, Ann Arbor, USA, 2022. Springer. details

  10. Mark Kotanchek and Nathan Haut. Back to the Future: Revisiting OrdinalGP and Trustable Models after a Decade. In Wolfgang Banzhaf and Leonardo Trujillo and Stephan Winkler and Bill Worzel editors, Genetic Programming Theory and Practice XVIII, pages 129-142, East Lansing, USA, 2021. Springer. details

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