Parameter Setting in Evolutionary Algorithms Edited by Fernando G. Lobo, Cláudio F. Lima, and Zbigniew Michalewicz Studies in Computational Intelligence. Springer, 2007. You can order the book from Springer or amazon.com.

About the book

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

1. Parameter Setting in EAs: a 30 Year Perspective
   Ken De Jong
2. Parameter Control in Evolutionary Algorithms
   Agoston Eiben, Zbigniew Michalewicz, Marc Schoenauer, James Smith
3. Self-Adaptation in Evolutionary Algorithms
   Silja Meyer-Nieberg, Hans-Georg Beyer
4. Adaptive Strategies for Operator Allocation
   Dirk Thierens
5. Sequential Parameter Optimization Applied to Self-Adaptation for Binary Coded Evolutionary Algorithms
   Mike Preuss, Thomas Bartz-Beielstein
6. Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks
   Bo Yuan, Marcus Gallagher
7. Genetic Programming: Parametric Analysis of Structure Altering Mutation Techniques
   Alan Piszcz, Terrence Soule
8. Parameter Sweeps for Exploring Parameters Spaces of Genetic and Evolutionary Algorithms
   Michael Samples, Matt Byom, Jason Daida
9. Adaptive Population Sizing Schemes in Genetic Algorithms
   Fernando Lobo, Cláudio Lima
10. Population Sizing to Go: Online Adaptation Using Noise and Substructural Measurements
    Tian-Li Yu, Kumara Sastry, David Goldberg
11. Parameter-less Hierarchical Bayesian Optimization Algorithm
    Martin Pelikan, Alexander Hartmann, Tz-Kai Lin
12. Evolutionary Multi-Objective Optimization Without Additional Parameters
    Kalyanmoy Deb
13. Parameter Setting in Parallel Genetic Algorithms
    Erick Cantú-Paz
14. Parameter Control in Practice
    Zbigniew Michalewicz, Martin Schmidt
15. Parameter Adaptation for GP Forecasting Applications
    Neal Wagner, Zbigniew Michalewicz