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Genetic Programming: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 1517, 2020, Proceedings 1st ed. 2020 [Pehme köide]

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  • Formaat: Paperback / softback, 295 pages, kõrgus x laius: 235x155 mm, kaal: 474 g, 72 Illustrations, color; 85 Illustrations, black and white; X, 295 p. 157 illus., 72 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 12101
  • Ilmumisaeg: 13-Mar-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030440931
  • ISBN-13: 9783030440930
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Genetic Programming: 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 1517, 2020, Proceedings 1st ed. 2020Suurem pilt
  • Formaat: Paperback / softback, 295 pages, kõrgus x laius: 235x155 mm, kaal: 474 g, 72 Illustrations, color; 85 Illustrations, black and white; X, 295 p. 157 illus., 72 illus. in color., 1 Paperback / softback
  • Sari: Lecture Notes in Computer Science 12101
  • Ilmumisaeg: 13-Mar-2020
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030440931
  • ISBN-13: 9783030440930
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030440930.html
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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.

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.