Parallel Problem Solving from Nature - PPSN XVI [electronic resource] : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II / edited by Thomas Bäck, Mike Preuss, André Deutz, Hao Wang, Carola Doerr, Michael Emmerich, Heike Trautmann.
Contributor(s): Bäck, Thomas [editor.] | Preuss, Mike [editor.] | Deutz, André [editor.] | Wang, Hao [editor.] | Doerr, Carola [editor.] | Emmerich, Michael [editor.] | Trautmann, Heike [editor.] | SpringerLink (Online service).
Material type: BookSeries: Theoretical Computer Science and General Issues: 12270Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XXIX, 717 p. 318 illus., 146 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783030581152.Subject(s): Computer science | Artificial intelligence | Computer science -- Mathematics | Discrete mathematics | Software engineering | Mathematical statistics | Theory of Computation | Artificial Intelligence | Mathematics of Computing | Discrete Mathematics in Computer Science | Software Engineering | Probability and Statistics in Computer ScienceAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 004.0151 Online resources: Click here to access onlineGenetic Programming -- Landscape Analysis -- Multiobjective Optimization -- Real-World Applications -- Reinforcement Learning -- Theoretical Aspects of Nature-Inspired Optimization. .
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.
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