Theory of Evolutionary Computation
Recent Developments in Discrete Optimization
Editors: Doerr, Benjamin, Neumann, Frank (Eds.)
Free Preview- 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
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- About this book
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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 influences performance. 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.
- Table of contents (11 chapters)
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Probabilistic Tools for the Analysis of Randomized Optimization Heuristics
Pages 1-87
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Drift Analysis
Pages 89-131
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Complexity Theory for Discrete Black-Box Optimization Heuristics
Pages 133-212
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Parameterized Complexity Analysis of Randomized Search Heuristics
Pages 213-248
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Analysing Stochastic Search Heuristics Operating on a Fixed Budget
Pages 249-270
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Table of contents (11 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Theory of Evolutionary Computation
- Book Subtitle
- Recent Developments in Discrete Optimization
- Editors
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- Benjamin Doerr
- Frank Neumann
- Series Title
- Natural Computing Series
- Copyright
- 2020
- Publisher
- Springer International Publishing
- Copyright Holder
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-29414-4
- DOI
- 10.1007/978-3-030-29414-4
- Hardcover ISBN
- 978-3-030-29413-7
- Softcover ISBN
- 978-3-030-29416-8
- Series ISSN
- 1619-7127
- Edition Number
- 1
- Number of Pages
- XII, 506
- Number of Illustrations
- 10 b/w illustrations, 17 illustrations in colour
- Topics
