Parallel Problem Solving from Nature - PPSN XVI

Thomas Bäck

ISBN 10: 3030581144 / ISBN 13: 9783030581145

Verlag: Springer International Publishing, Springer Nature Switzerland Sep 2020, 2020

Beschreibung

Neuware -This two-volume set LNCS 12269 and LNCS 12270 constitutes the 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 748 pp. Englisch.

Bestandsnummer des Verkäufers 9783030581145

Bibliografische Details

• Titel: Parallel Problem Solving from Nature - PPSN XVI
• Verlag: Springer International Publishing, Springer Nature Switzerland Sep 2020
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 3030581144
• ISBN-13: 9783030581145
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

This two-volume set LNCS 12269 and LNCS 12270 constitutes the 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.