***Please accept my apologies if you have received multiple copies of this
announcement***
Dear Colleagues,
I would like to announce a new book edited by Fernando Lobo,
Zbigniew Michalewicz, and myself. The book belongs to the Springer
Series "Studies in Computational Intelligence".
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Parameter Setting in Evolutionary Algorithms
Edited by Fernando G. Lobo, Cláudio F. Lima, and Zbigniew Michalewicz
Studies in Computational Intelligence. Springer, 2007.
http://www.springer.com/east/home/engineering?SGWID=5-175-22-173712006-0
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Description
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.
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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
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