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...
Clasificación: | Libro Electrónico |
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Autor Corporativo: | |
Otros Autores: | , , |
Formato: | Electrónico eBook |
Idioma: | Inglés |
Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2007.
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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.