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Genetic Programming de Mario Giacobini

Genetic Programming: Lecture Notes in Computer Science

Editat de Mario Giacobini, Bing Xue, Luca Manzoni
en Limba Engleză Paperback – 17 apr 2024
This book constitutes the refereed proceedings of the 27th European Conference on Genetic Programming, EuroGP 2024, held in Aberystwyth, UK, April 3–5, 2024 and co-located with the EvoStar events, EvoCOP, EvoMUSART, and EvoApplications.


The 13 papers (9 selected for long presentation and 4 for short presentation) collected in this book were carefully reviewed and selected from 24 submissions. The wide range of topics in this volume reflects the current state of research in the field. The collection of papers cover topics including developing new variants of GP algorithms, as well as exploring GP applications to the optimization of machine learning methods and the evolution of control policies.
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Specificații

ISBN-13: 9783031569562
ISBN-10: 3031569563
Pagini: 240
Ilustrații: X, 227 p. 69 illus., 53 illus. in color.
Dimensiuni: 155 x 235 x 14 mm
Greutate: 0.37 kg
Ediția:2024
Editura: Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

Long Presentations.- Fuzzy Pattern Trees for Classification Problems Using Genetic Programming.- Generational Computation Reduction in Informal Counterexample-Driven Genetic Programming.- Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots.- Grammar-based Evolution of Polyominoes.- Naturally Interpretable Control Policies via Graph-based Genetic Programming.- DALex: Lexicase-like Selection via Diverse Aggregation.- Enhancing Large Language Models-based Code Generation by Leveraging Genetic Improvement.- SLIM GSGP: The Non-Bloating Geometric Semantic Genetic Programming.- Improving Generalization of Evolutionary Feature Construction with Minimal Complexity Knee Points in Regression.- Short Presentations.- Look into the Mirror: Evolving Self-Dual Bent Boolean Functions.-An Algorithm Based on Grammatical Evolution for Discovering SHACL Constraints.- A Comprehensive Comparison of Lexicase-Based Selection Methods for Symbolic Regression Problems.- Improvement of Last Level Cache.