Theory of Evolutionary Computation : Recent Developments in Discrete Optimization Hardback - 2019
by Benjamin Doerr (Editor); Frank Neumann (Editor)
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- Hardback
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Details
- Title Theory of Evolutionary Computation : Recent Developments in Discrete Optimization
- Author Benjamin Doerr (Editor); Frank Neumann (Editor)
- Binding Hardback
- Condition New
- Pages 506
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2019-12-04
- Illustrated Yes
- Features Illustrated
- Bookseller's Inventory # ria9783030294137_inp
- ISBN 9783030294137 / 3030294137
- Weight 2.02 lbs (0.92 kg)
- Dimensions 9.21 x 6.14 x 1.13 in (23.39 x 15.60 x 2.87 cm)
- Category Computers - General Information
- Dewey Decimal Code 004.015
- Quantity available 190
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From the publisher
From the rear cover
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.
