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9783540694311

Parameter Setting in Evolutionary Algorithms

by ; ;
  • ISBN13:

    9783540694311

  • ISBN10:

    3540694315

  • Format: Hardcover
  • Copyright: 2007-04-05
  • Publisher: Springer Verlag

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Supplemental Materials

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Summary

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.

Table of Contents

Parameter setting in EAs : a 30 year perspectivep. 1
Parameter control in evolutionary algorithmsp. 19
Self-adaptation in evolutionary algorithmsp. 47
Adaptive strategies for operator allocationp. 77
Sequential parameter optimization applied to self-adaptation for binary-coded evolutionary algorithmsp. 91
Combining meta-EAs and racing for difficult EA parameter tuning tasksp. 121
Genetic programming : parametric analysis of structure altering mutation techniquesp. 143
Parameter sweeps for exploring parameter spaces of genetic and evolutionary algorithmsp. 161
Adaptive population sizing schemes in genetic algorithmsp. 185
Population sizing to go : online adaptation using noise and substructural measurementsp. 205
Parameter-less hierarchical Bayesian optimization algorithmp. 225
Evolutionary multi-objective optimization without additional parametersp. 241
Parameter setting in parallel genetic algorithmsp. 259
Parameter control in practicep. 277
Parameter adaptation for GP forecasting applicationsp. 295
Table of Contents provided by Blackwell. All Rights Reserved.

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The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

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