Details

Title Theory of Evolutionary Computation: Recent Developments in Discrete Optimization (Natural Computing Series)
Author Doerr, Benjamin
Binding Hardback
Edition 1st ed. 2020
Condition Used: Good
Pages 506
Volumes 1
Language ENG
Publisher Springer
Publication date 2019-12-04
Illustrated Yes
Features Illustrated
Bookseller's Inventory # SONG3030294137
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)
Size 6.14x1.13x9.21
Category Computers - General Information
Dewey Decimal Code 004.015
Quantity available 1

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From the publisher

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.

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.

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