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...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2007, 2007
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| Edition: | 1st ed. 2007 |
| Series: | Studies in Computational Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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