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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, operato...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Lobo, F.J (Editor ), Lima, Cláudio F. (Editor ), Michalewicz, Zbigniew (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.
Edición:1st ed. 2007.
Colección:Studies in Computational Intelligence, 54
Temas:
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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.