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Parameter Setting in Evolutionary Algorithms
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Parameter Setting in Evolutionary Algorithms - hardcover

2007, ISBN: 9783540694311

Gebunden, 332 Seiten, 241mm x 160mm x 23mm, Sprache(n): eng Recent advances in evolutionary algorithmsIncludes supplementary material: sn.pub/extrasOne of the main difficulties of applyin… More...

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Parameter Setting in Evolutionary Algorithms - Fernando G.. Lobo#Cláudio F. Lima#Zbigniew Michalewicz
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Fernando G.. Lobo#Cláudio F. Lima#Zbigniew Michalewicz:

Parameter Setting in Evolutionary Algorithms - hardcover

2007, ISBN: 9783540694311

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 value… More...

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Parameter Setting in Evolutionary Algorithms F.J. Lobo Editor
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Parameter Setting in Evolutionary Algorithms F.J. Lobo Editor - new book

ISBN: 9783540694311

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 value… More...

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Parameter Setting in Evolutionary Algorithms - F.J. Lobo; Cláudio F. Lima; Zbigniew Michalewicz
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F.J. Lobo; Cláudio F. Lima; Zbigniew Michalewicz:
Parameter Setting in Evolutionary Algorithms - hardcover

ISBN: 9783540694311

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 value… More...

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Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence, 54, Band 54)
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Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence, 54, Band 54) - hardcover

2007, ISBN: 9783540694311

Springer, Gebundene Ausgabe, Auflage: 2007, 330 Seiten, Publiziert: 2007-03-16T00:00:01Z, Produktgruppe: Buch, Hersteller-Nr.: 100 black & white illustrations, 24 blac, 1.46 kg, Informati… More...

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Details of the book
Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence, 54, Band 54)

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. TOC:Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.

Details of the book - Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence, 54, Band 54)


EAN (ISBN-13): 9783540694311
ISBN (ISBN-10): 3540694315
Hardcover
Publishing year: 2007
Publisher: Lobo, F.J. Lima, Cláudio F. Michalewicz, Zbigniew, Springer

Book in our database since 2007-06-05T22:28:05+01:00 (London)
Detail page last modified on 2024-01-31T13:35:39+00:00 (London)
ISBN/EAN: 9783540694311

ISBN - alternate spelling:
3-540-69431-5, 978-3-540-69431-1
Alternate spelling and related search-keywords:
Book author: lobo, zbigniew, michale, claudio lima
Book title: parameter setting, evolutionary intelligence, studies computational intelligence, algorithms


Information from Publisher

Author: F.J. Lobo; Cláudio F. Lima; Zbigniew Michalewicz
Title: Studies in Computational Intelligence; Parameter Setting in Evolutionary Algorithms
Publisher: Springer; Springer Berlin
318 Pages
Publishing year: 2007-03-16
Berlin; Heidelberg; DE
Printed / Made in
Weight: 0,664 kg
Language: English
213,99 € (DE)
219,99 € (AT)
236,00 CHF (CH)
POD
XII, 318 p. 100 illus.

BB; Mathematical and Computational Engineering; Hardcover, Softcover / Technik/Allgemeines, Lexika; Mathematik für Ingenieure; Verstehen; algorithm; algorithms; calculus; control; evolution; evolutionary algorithm; evolutionary computation; evolutionary strategies; genetic algorithms; genetic programming; multi-objective optimization; mutation; operator; optimization; programming; Artificial Intelligence; Mathematical and Computational Engineering Applications; Artificial Intelligence; Künstliche Intelligenz; BC; EA

Parameter Setting in EAs: a 30 Year Perspective.- Parameter Control in Evolutionary Algorithms.- Self-Adaptation in Evolutionary Algorithms.- Adaptive Strategies for Operator Allocation.- Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms.- Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks.- Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques.- Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms.- Adaptive Population Sizing Schemes in Genetic Algorithms.- Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements.- Parameter-less Hierarchical Bayesian Optimization Algorithm.- Evolutionary Multi-Objective Optimization Without Additional Parameters.- Parameter Setting in Parallel Genetic Algorithms.- Parameter Control in Practice.- Parameter Adaptation for GP Forecasting Applications.

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