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Parameter Setting in Evolutionary Algorithms
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Parameter Setting in Evolutionary Algorithms - encuadernado, tapa blanda

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… Más…

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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 - encuadernado, tapa blanda

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… Más…

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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 - libro nuevo

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… Más…

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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 - encuadernado, tapa blanda

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… Más…

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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) - encuadernado, tapa blanda

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… Más…

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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.

Detalles del libro - Parameter Setting in Evolutionary Algorithms (Studies in Computational Intelligence, 54, Band 54)


EAN (ISBN-13): 9783540694311
ISBN (ISBN-10): 3540694315
Tapa dura
Año de publicación: 2007
Editorial: Lobo, F.J. Lima, Cláudio F. Michalewicz, Zbigniew, Springer

Libro en la base de datos desde 2007-06-05T16:28:05-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-04-21T08:33:15-06:00 (Mexico City)
ISBN/EAN: 9783540694311

ISBN - escritura alterna:
3-540-69431-5, 978-3-540-69431-1
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: lobo, zbigniew, michale, claudio lima
Título del libro: parameter setting, evolutionary intelligence, studies computational intelligence, algorithms


Datos del la editorial

Autor: F.J. Lobo; Cláudio F. Lima; Zbigniew Michalewicz
Título: Studies in Computational Intelligence; Parameter Setting in Evolutionary Algorithms
Editorial: Springer; Springer Berlin
318 Páginas
Año de publicación: 2007-03-16
Berlin; Heidelberg; DE
Idioma: Inglés
213,99 € (DE)
219,99 € (AT)
236,00 CHF (CH)
Available
XII, 318 p. 100 illus.

BB; 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; Mathematical and Computational Engineering Applications; Artificial Intelligence; Künstliche Intelligenz; BC

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.
Recent advances in evolutionary algorithms Includes supplementary material: sn.pub/extras

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