Genetic programming 29th European Conference, EuroGp 2026 held as part of EvoStar 2026 Toulouse, France, April 8-10, 2026 proceedings Luca Manzoni, Sylvain Cussat-Blanc, Qi Chen, editors

Format:

Book

Conference/Event

Contributor:

Manzoni, Luca, editor.

Cussat-Blanc, Sylvain, 1984- editor.

Chen, Qi (Of Victoria University of Wellington), editor.

Conference Name:

EuroGp 2026 (2026 : Toulouse, France)

Series:

Lecture notes in computer science ; 16521.

Lecture notes in computer science 1611-3349 16521

Language:

English

Subjects (All):

Genetic programming (Computer science).

Genre:

proceedings (reports)

Conference papers and proceedings

Conference papers and proceedings.

Physical Description:

1 online resource

Place of Publication:

Cham, Switzerland Springer [2026]

Summary:

"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"-- Springer Nature Link

Contents:

On the effects of down-sampling for tournament and lexicase selection in program synthesis / Martin Briesch

Comparison of parent and environmental selection schemes in genetic programming / Vladimir Stanovov

A comparative study on robustness in evolved image classifiers / Camilo De La Torre, Stéphane Treillard, Camille Franchet, Hervé Luga, Dennis Wilson, and Sylvain Cussat-Blanc

Syntactic flexibility enables compact solutions in transformer semantic GP / Philipp Anthes

Node preservation and its effect on crossover in cartesian genetic programming / Mark Kocherovsky, Illya Bakurov, and Wolfgang Banzhaf

New perspectives on cartesian genetic programming : A survey / Mark Kocherovsky, Henning Cui, Illya Bakurov, Michael Heider, Roman Kalkreuth, and Wolfgang Banzhaf

Semantic search trajectory networks for understanding genetic programming / Josip Hrvatic, Magda Smolic-Rocak, Marko Ðurasevic, and Gabriela Ochoa

A hybrid LLM-coevolution framework to generate abusive tax strategies / Joy Sera Bhattacharya, Erik Hemberg, and Una-May O’Reilly

Sinking the bloat in genetic programming using equality saturation / Lucas Miranda, Matheus Fernandes, Emilio Francesquini, and Fabricio Olivetti de Franca

Revisiting SLIM : Improved learning dynamics and model compactness in symbolic regression / Gorka Silva, Lachlan Stewart, Illya Bakurov, Mauro Castelli, Davide Farinati, Jose Manuel Muñoz Contreras, Leonardo Trujillo, and Leonardo Vanneschi

Dynamic vector and matrix memory for tangled program graphs / Ali Naqvi and Stephen Kelly

Extending model selection criteria with extrapolation and sensitivity penalties for symbolic regression / Fitria Wulandari Ramlan, Colm O’Riordan, and James McDermott

Optimal mixing in graph-based GP for control : Genotypical dependencies are hardly captured / Giorgia Nadizar, Gloria Pietropolli, and Eric Medvet

Multi-tree genetic programming with semantic complementarity for feature construction in symbolic regression / Jiayu Zhang, Qi Chen, Bing Xue, and Mengjie Zhang

NEVO-GSPT : Population-based neural network evolution using inflate and deflate operators / Davide Farinati, Frederico J. J. B. Santos, Leonardo Vanneschi, and Mauro Castelli

Multi-action tangled program graphs for multi-task reinforcement learning with continuous control / Quentin Vacher, Nicolas Beuve, Mickaël Dardaillon, and Karol Desnos

Reducing computational overhead in biomedical image segmentation via active learning and PCA-based diversity filtering in CGP / Yuri Lavinas, Nathaniel Haut, Sylvain Cussat-Blanc, and Wolfgang Banzhaf

Using Monte Carlo tree search to enhance search space exploration in cartesian genetic programming / Christina Berghegger, Camilo De La Torre, Sylvain Cussat-Blanc, Yuri Lavinas, and David Simoncini

Extended semantics operator for genetic programming : A semantic-density approach to improve model robustness / Sofia Pereira and Leonardo Vanneschi

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (Springer Nature Link, viewed May 4, 2026)

Other Format:

Print version EuroGp 2026 (2026 : Toulouse, France) Genetic programming

ISBN:

9783032230058

3032230055

OCLC:

1587201078

Access Restriction:

Restricted for use by site license