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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Theory of Evolutionary Computation: Recent Developments in Discrete Optimization

Theory of Evolutionary Computation: Recent Developments in Discrete Optimization

Doerr, Benjamin
Neumann, Frank

Paperback

Series: Natural Computing

General ComputersGeneral Mathematics

ISBN10: 3030294161
ISBN13: 9783030294168
Publisher: Springer Nature
Published: Dec 3 2020
Pages: 506
Weight: 1.62
Height: 1.07 Width: 6.14 Depth: 9.21
Language: English

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.

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