Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I
Author:   Thomas Bäck ,  Mike Preuss ,  André Deutz ,  Hao Wang
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12269
ISBN:  

9783030581114


Pages:   735
Publication Date:   03 September 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Parallel Problem Solving from Nature – PPSN XVI: 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part I


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Overview

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.

Full Product Details

Author:   Thomas Bäck ,  Mike Preuss ,  André Deutz ,  Hao Wang
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12269
Weight:   1.157kg
ISBN:  

9783030581114


ISBN 10:   303058111
Pages:   735
Publication Date:   03 September 2020
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

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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