Manzoni / Cussat-Blanc / Chen Genetic Programming

29th European Conference, EuroGP 2026, Held as Part of EvoStar 2026, Toulouse, France, April 8–10, 2026, Proceedings
Erscheinungsjahr 2026
ISBN: 978-3-032-23005-8
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 322 Seiten

Reihe: Springer Nature Proceedings Computer Science

This book constitutes the refereed proceedings of the 29th European Conference on Genetic Programming, EuroGP 2026, held as part of EvoStar 2026 in Toulouse, France, during April 8-10, 2026.

The 12 full papers were and 7 short papers included in this volume were carefully reviewed and selected from 34 submissions. The conference presents topics such as Genetic Programming, Evolutionary Computation, Symbolic Regression, Program Synthesis, Evolutionary Machine Learning, Explainable Artificial Intelligence, Interpretable Models and much more. 

Zielgruppe

Research

Autoren/Hrsg.

Manzoni, Luca

Cussat-Blanc, Sylvain

Chen, Qi

Inhaltsverzeichnis

.- Long Presentation
.- On the Effects of Down-Sampling for Tournament and Lexicase Selection in Program Synthesis.
.- Comparison of Parent and Environmental Selection Schemes in Genetic Programming.
.- A Comparative Study on Robustness in Evolved Image Classifiers.
.- Syntactic Flexibility Enables Compact Solutions in Transformer Semantic GP.
.- Node Preservation and Its Effect on Crossover in Cartesian Genetic Programming.
.- New Perspectives on Cartesian Genetic Programming: A Survey.
.- Semantic Search Trajectory Networks for Understanding Genetic Programming.
.- A Hybrid LLM-Coevolution Framework to Generate Abusive Tax Strategies.
.- Sinking the Bloat in Genetic Programming Using Equality Saturation.
.- Revisiting SLIM: Improved Learning Dynamics and Model Compactness in Symbolic Regression.
.- Dynamic Vector and Matrix Memory for Tangled Program Graphs.
.- Extending Model Selection Criteria with Extrapolation and Sensitivity Penalties for Symbolic Regression.
.- Short Presentation
.- Optimal Mixing in Graph-Based GP for Control: Genotypical Dependencies Are Hardly Captured.
.- Multi-tree Genetic Programming with Semantic Complementarity for Feature Construction in Symbolic Regression.
.- NEVO-GSPT: Population-Based Neural Network Evolution Using Inflate and Deflate Operators.
.- Multi-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control.
.- Reducing Computational Overhead in Biomedical Image Segmentation via Active Learning and PCA-Based Diversity Filtering in CGP.
.- Using Monte Carlo Tree Search to Enhance Search Space Exploration in Cartesian Genetic Programming.
.- Extended Semantics Operator for Genetic Programming: A Semantic-Density Approach to Improve Model Robustness.