Parameter Setting in Evolutionary Algorithms

F. J. Lobo

ISBN 10: 3540694315 / ISBN 13: 9783540694311

Published by Springer Berlin Heidelberg Mrz 2007, 2007

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. 332 pp. Englisch.

Seller Inventory # 9783540694311

Bibliographic Details

• Title: Parameter Setting in Evolutionary Algorithms
• Publisher: Springer Berlin Heidelberg Mrz 2007
• Publication Date: 2007
• Language: English
• ISBN 10: 3540694315
• ISBN 13: 9783540694311
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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.