Genetic Programming (eBook, PDF)

24th European Conference, EuroGP 2021, Held as Part of EvoStar 2021, Virtual Event, April 7-9, 2021, Proceedings
Redaktion: Hu, Ting; Medvet, Eric; Lourenço, Nuno

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 24th European Conference on Genetic Programming, EuroGP 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events, EvoCOP, EvoMUSART, and EvoApplications. The 11 revised full papers and 6 short papers presented in this book 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 collection of papers cover interesting topics including developing new operators for variants of GP algorithms, as well as exploring GP applications to the optimisation of machine learning methods and the evolution of complex combinational logic circuits.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 281
• Erscheinungstermin: 24. März 2021
• Englisch
• ISBN-13: 9783030728120
• Artikelnr.: 62287712

Inhaltsangabe

Quality Diversity Genetic Programming for Learning Decision Tree Ensembles.- Progressive Insular Cooperative GP.- Regenerating Soft Robots through Neural Cellular Automata.- Inclusive Genetic Programming.- Towards incorporating Human Knowledge in Fuzzy Pattern Tree Evolution.- Evolutionary Neural Architecture Search Supporting Approximate Multipliers.- Automatic design of deep neural networks applied to image segmentation problems.- On the Influence of Grammars on Crossover in Grammatical Evolution.- On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming.- Evolution of Complex Combinational Logic Circuits Using Grammatical Evolution with SystemVerilog.- Evofficient: Reproducing a Cartesian Genetic Programming Method.- Software Anti-patterns Detection Under Uncertainty Using A Possibilistic Evolutionary Approach.- Probabilistic Grammatical Evolution.- Evolving allocation rules for beam search heuristics in assembly line balancing.- Incremental Evaluation of Genetic Programming.- Mining Feature Relationships in Data.- Getting a Head Start on Program Synthesis with Genetic Programming.