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Record Nr. |
UNISA996418219703316 |
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Titolo |
Genetic Programming [[electronic resource] ] : 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings / / edited by Ting Hu, Nuno Lourenço, Eric Medvet, Federico Divina |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (306 pages) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 12101 |
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Disciplina |
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Soggetti |
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Computer science |
Computer systems |
Computers, Special purpose |
Computer networks |
Artificial intelligence |
Computer engineering |
Theory of Computation |
Computer System Implementation |
Special Purpose and Application-Based Systems |
Computer Communication Networks |
Artificial Intelligence |
Computer Engineering and Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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 |
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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. |
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Sommario/riassunto |
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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. |
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