Parameter Setting in Evolutionary Algorithms

✍ Scribed by Kenneth De Jong (auth.), Fernando G. Lobo, Cláudio F. Lima, Zbigniew Michalewicz (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2007
• Tongue: English
• Leaves: 327
• Series: Studies in Computational Intelligence 54
• Edition: 1
• Category: Library

✦ Synopsis

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

Front Matter....Pages I-XII
Parameter Setting in EAs: a 30 Year Perspective....Pages 1-18
Parameter Control in Evolutionary Algorithms....Pages 19-46
Self-Adaptation in Evolutionary Algorithms....Pages 47-75
Adaptive Strategies for Operator Allocation....Pages 77-90
Sequential Parameter Optimization Applied to Self-Adaptation for Binary-Coded Evolutionary Algorithms....Pages 91-119
Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks....Pages 121-142
Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques....Pages 143-160
Parameter Sweeps for Exploring Parameter Spaces of Genetic and Evolutionary Algorithms....Pages 161-184
Adaptive Population Sizing Schemes in Genetic Algorithms....Pages 185-204
Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements....Pages 205-223
Parameter-less Hierarchical Bayesian Optimization Algorithm....Pages 225-239
Evolutionary Multi-Objective Optimization Without Additional Parameters....Pages 241-257
Parameter Setting in Parallel Genetic Algorithms....Pages 259-276
Parameter Control in Practice....Pages 277-294
Parameter Adaptation for GP Forecasting Applications....Pages 295-309
Back Matter....Pages 311-317

✦ Subjects

Appl.Mathematics/Computational Methods of Engineering; Artificial Intelligence (incl. Robotics)