Fr. 82.00

Genetic Programming - 28th European Conference, EuroGP 2025, Held as Part of EvoStar 2025, Trieste, Italy, April 23-25, 2025, Proceedings

Inglese · Tascabile

Spedizione di solito entro 6 a 7 settimane

Descrizione

Ulteriori informazioni

This book constitutes the refereed proceedings of the 28th European Conference on Genetic Programming, EuroGP 2025, held in Trieste, Italy, during April 23 25, 2025 and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications.
The 10 full papers were and 5 short papers included in this volume were carefully reviewed and selected from 27 submissions.The wide range of topics in this volume reflects the current state of research in the field. The universality of computer programs and their importance in so many areas of our lives means that automating these tasks is an exceptionally ambitious challengewith far-reaching implications.

Sommario

.-  Ghost Swarms: Learning Swarm Rules from Environmental Changes Alone.
.- A Systematic Evaluation of Evolving Highly Nonlinear Boolean
Functions in Odd Sizes.
.- Exploring the Impact of Data Scale on Mutation Step Size in SLIM-GSGP.
.- Multi-Objective Evolutionary Design of Explainable EEG Classifier.
.- On the Effectiveness of Crossover Operators in Cartesian Genetic
Programming.
.- Population Diversity, Information Theory and Genetic Improvement.
.- Introducing Crossover in SLIM-GSGP.
.- Exploring the Integration of Cellular Structures in Genetic
Programming-based Methods.
.- Ant-based Metaheuristics Struggle to Solve the Cartesian Genetic
Programming Learning Task.
.- Designing Lookahead Relocation Rules for the Container Relocation
Problem with Genetic Programming.
.- Evolved and Transparent Pipelines for Biomedical Image Classification.
.- Unified Piecewise Symbolic Regression.
.- Was Tournament Selection All We Ever Needed? A Critical Reflection
on Lexicase Selection.
.- The Role of Stepping Stones in MAP-Elites: Insights from Search
Trajectory Networks.
.- Micro-Step Time-Series Regression: Insights from System Identification
Using Symbolic Regression.

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