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Record Nr. |
UNISA996418291703316 |
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Titolo |
Parallel Problem Solving from Nature – PPSN XVI [[electronic resource] ] : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I / / edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann |
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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 (XXIX, 735 p. 261 illus., 169 illus. in color.) |
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Collana |
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Theoretical Computer Science and General Issues, , 2512-2029 ; ; 12269 |
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Disciplina |
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Soggetti |
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Computer science |
Artificial intelligence |
Computer science—Mathematics |
Discrete mathematics |
Mathematical statistics |
Computer networks |
Theory of Computation |
Artificial Intelligence |
Mathematics of Computing |
Discrete Mathematics in Computer Science |
Probability and Statistics in Computer Science |
Computer Communication 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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Note generali |
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Nota di contenuto |
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Automated Algorithm Selection and Configuration -- Bayesian- and Surrogate-Assisted Optimization -- Benchmarking and Performance Measures -- Combinatorial Optimization -- Connection Between Nature-Inspired Optimization and Artificial Intelligence -- Genetic and Evolutionary Algorithms. |
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Sommario/riassunto |
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This two-volume set LNCS 12269 and LNCS 12270 constitutes the |
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refereed proceedings of the 16th International Conference on Parallel Problem Solving from Nature, PPSN 2020, held in Leiden, The Netherlands, in September 2020. The 99 revised full papers were carefully reviewed and selected from 268 submissions. The topics cover classical subjects such as automated algorithm selection and configuration; Bayesian- and surrogate-assisted optimization; benchmarking and performance measures; combinatorial optimization; connection between nature-inspired optimization and artificial intelligence; genetic and evolutionary algorithms; genetic programming; landscape analysis; multiobjective optimization; real-world applications; reinforcement learning; and theoretical aspects of nature-inspired optimization. |
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