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Parameter Setting in Evolutionary Algorithms - Lobo, Fernando G. / Lima, Claudio F. / Michalewicz, Zbigniew (eds.)
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Lobo, Fernando G. / Lima, Claudio F. / Michalewicz, Zbigniew (eds.):
Parameter Setting in Evolutionary Algorithms - hardcover

2007, ISBN: 9783540694311

[ED: Hardcover], [PU: Springer, Berlin], 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. 2007. xii, 318 S. 100 SW-Abb., 24 Tabellen. 235 mm Versandfertig in 3-5 Tagen, DE, [SC: 0.00], Neuware, gewerbliches Angebot, offene Rechnung (Vorkasse vorbehalten)

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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 - new book

2007, ISBN: 9783540694311

ID: 41011295

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. Parameter Setting in Evolutionary Algorithms Bücher > Fremdsprachige Bücher > Englische Bücher gebundene Ausgabe 16.03.2007 Buch (fremdspr.), Springer, .200

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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 - new book

2007, ISBN: 9783540694311

ID: 787474702

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. Parameter Setting in Evolutionary Algorithms Buch (fremdspr.) gebundene Ausgabe 16.03.2007 Bücher>Fremdsprachige Bücher>Englische Bücher, Springer, .200

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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 - new book

ISBN: 9783540694311

ID: 740748309

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. Parameter Setting in Evolutionary Algorithms Buch (fremdspr.) Bücher>Fremdsprachige Bücher>Englische Bücher, Springer

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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 - new book

ISBN: 9783540694311

ID: 9783540694311

Engineering; Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics) algorithm, algorithms, calculus, control, evolution, evolutionary algorithm, evolutionary computation, evolutionary strategies, genetic algorithms, genetic programming, multi-objective optimization, mutation, operator, optimization, programming Books Book, Springer Science+Business Media

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Details of the book
Parameter Setting in Evolutionary Algorithms

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.

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EAN (ISBN-13): 9783540694311
ISBN (ISBN-10): 3540694315
Hardcover
Publishing year: 2007
Publisher: Springer Berlin Heidelberg

Book in our database since 05.06.2007 23:28:05
Book found last time on 04.05.2018 16:17:41
ISBN/EAN: 9783540694311

ISBN - alternate spelling:
3-540-69431-5, 978-3-540-69431-1


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