Umfang:
1 Online-Ressource(X, 295 p. 157 illus., 72 illus. in color.)
Ausgabe:
1st ed. 2020.
ISBN:
9783030440947
Serie:
Theoretical Computer Science and General Issues 12101
Inhalt:
Hessian Complexity Measure for Genetic Programming-based Imputation Predictor Selection in Symbolic Regression with Incomplete Data -- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing -- Incremental Evolution and Development of Deep Artificial Neural Networks -- Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming -- Comparing Genetic Programming Approaches for Non-Functional Genetic Improvement -- Automatically Evolving Lookup Tables for Function Approximation -- Optimising Optimisers with Push GP -- An Evolutionary View on Reversible Shift-invariant Transformations -- Benchmarking Manifold Learning Methods on a Large Collection of Datasets -- Ensemble Genetic Programming -- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets -- Effect of Parent Selection Methods on Modularity -- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models -- Challenges of Program Synthesis with Grammatical Evolution -- Detection of Frailty Using Genetic Programming : The Case of Older People in Piedmont, Italy -- Is k Nearest Neighbours Regression Better than GP -- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling -- Classification of Autism Genes using Network Science and Linear Genetic Programming.
Inhalt:
This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications. The 12 full papers and 6 short papers presented in this book were carefully reviewed and selected from 36 submissions. The papers cover a wide spectrum of topics, including designing GP algorithms for ensemble learning, comparing GP with popular machine learning algorithms, customising GP algorithms for more explainable AI applications to real-world problems.
Weitere Ausg.:
ISBN 9783030440930
Weitere Ausg.:
ISBN 9783030440954
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe EuroGP (23. : 2020 : Online) Genetic programming Cham : Springer, 2020 ISBN 9783030440930
Weitere Ausg.:
Erscheint auch als Druck-Ausgabe ISBN 9783030440954
Sprache:
Englisch
Schlagwort(e):
Genetische Programmierung
;
Konferenzschrift
DOI:
10.1007/978-3-030-44094-7
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