Overview
- Many advances have been made in this field in the last ten years
- Concise summary of the state of the art for graduate students and researchers
- Book covers the development of more powerful methods, the solution of longstanding open problems, and the analysis of new heuristics
Part of the book series: Natural Computing Series (NCS)
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Table of contents (11 chapters)
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Front Matter
Keywords
About this book
This edited book reports on recent developments in the theory of evolutionary computation, or more generally the domain of randomized search heuristics.
It starts with two chapters on mathematical methods that are often used in the analysis of randomized search heuristics, followed by three chapters on how to measure the complexity of a search heuristic: black-box complexity, a counterpart of classical complexity theory in black-box optimization; parameterized complexity, aimed at a more fine-grained view of the difficulty of problems; and the fixed-budget perspective, which answers the question of how good a solution will be after investing a certain computational budget. The book then describes theoretical results on three important questions in evolutionary computation: how to profit from changing the parameters during the run of an algorithm; how evolutionary algorithms cope with dynamically changing or stochastic environments; and how population diversity influencesperformance. Finally, the book looks at three algorithm classes that have only recently become the focus of theoretical work: estimation-of-distribution algorithms; artificial immune systems; and genetic programming.
Throughout the book the contributing authors try to develop an understanding for how these methods work, and why they are so successful in many applications. The book will be useful for students and researchers in theoretical computer science and evolutionary computing.
Editors and Affiliations
Bibliographic Information
Book Title: Theory of Evolutionary Computation
Book Subtitle: Recent Developments in Discrete Optimization
Editors: Benjamin Doerr, Frank Neumann
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/978-3-030-29414-4
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-29413-7Published: 04 December 2019
Softcover ISBN: 978-3-030-29416-8Published: 03 December 2020
eBook ISBN: 978-3-030-29414-4Published: 20 November 2019
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XII, 506
Number of Illustrations: 10 b/w illustrations, 17 illustrations in colour
Topics: Theory of Computation, Artificial Intelligence, Optimization, Operations Research/Decision Theory