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GPTP 2022
- Leonardo Trujillo, Stephan M. Winkler, Sara Silva, Wolfgang Banzhaf:
Genetic Programming Theory and Practice XIX [GPTP 2022]. Springer 2023, ISBN 978-981-19-8459-4 - Bogdan Burlacu, Michael Kommenda, Gabriel Kronberger, Stephan M. Winkler, Michael Affenzeller:
Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data. 1-30 - Nathan Haut, Wolfgang Banzhaf, Bill Punch:
Correlation Versus RMSE Loss Functions in Symbolic Regression Tasks. 31-55 - Robert Gold, Andrew Haydn Grant, Erik Hemberg, Chathika Gunaratne, Una-May O'Reilly:
GUI-Based, Efficient Genetic Programming and AI Planning for Unity3D. 57-79 - Ting Hu:
Genetic Programming for Interpretable and Explainable Machine Learning. 81-90 - Mark E. Kotanchek, Theresa Kotanchek, Kelvin Kotanchek:
Biological Strategies ParetoGP Enables Analysis of Wide and Ill-Conditioned Data from Nonlinear Systems. 91-116 - Penousal Machado, Francisco Baeta, Tiago Martins, João Correia:
GP-Based Generative Adversarial Models. 117-140 - Gustavo Olague, Matthieu Olague, Gerardo Ibarra-Vázquez, Isnardo Reducindo, Aaron Barrera, Axel Martinez, José Luis Briseño:
Modeling Hierarchical Architectures with Genetic Programming and Neuroscience Knowledge for Image Classification Through Inferential Knowledge. 141-166 - Susan Stepney:
Life as a Cyber-Bio-Physical System. 167-200 - Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri:
STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison. 201-231 - Alden H. Wright, Cheyenne L. Laue:
Evolving Complexity is Hard. 233-253 - Bill Worzel:
ESSAY: Computers Are Useless ... They Only Give Us Answers. 255-260
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