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Parameter setting in evolutionary algorithms: with 100 figures and 24 tables

Contributor(s): Lobo, Fernando G [Editor] | Lima, Cláudio F [Editor] | Michalewicz, Zbigniew [Editor].
Material type: materialTypeLabelBookSeries: Studies in Computational Intellgence, Volume 54. Publisher: Heidelberg Springer 2007Description: xii, 317 p. With index.ISBN: 9783540694311.DDC classification: 519.62 Summary: One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods. https://www.springer.com/in/book/9783540694311
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Books Vikram Sarabhai Library
Slot 1680 (2 Floor, East Wing) Non-fiction 519.62 P2 (Browse shelf) Available 197768

One of the main difficulties of applying an evolutionary algorithm (or, as a matter of fact, any heuristic method) to a given problem is to decide on an appropriate set of parameter values. Typically these are specified before the algorithm is run and include population size, selection rate, operator probabilities, not to mention the representation and the operators themselves. This book gives the reader a solid perspective on the different approaches that have been proposed to automate control of these parameters as well as understanding their interactions. The book covers a broad area of evolutionary computation, including genetic algorithms, evolution strategies, genetic programming, estimation of distribution algorithms, and also discusses the issues of specific parameters used in parallel implementations, multi-objective evolutionary algorithms, and practical consideration for real-world applications. It is a recommended read for researchers and practitioners of evolutionary computation and heuristic methods.

https://www.springer.com/in/book/9783540694311

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