Efficient and Accurate Parallel Genetic Algorithms by Erick Cantú-Paz Kluwer Academic Publishers (now Springer) ISBN 0-7923-7221-2 Volume 1 of the Book Series on Genetic Algorithms and Evolutionary Computation Now in its second printing!

"I urgently recommend that all readers interested in parallel genetic and evolutionary algorithms study this important book carefully and soon." ---David E. Goldberg

You can download the frontmatter (title page, table of contents, preface, and acknowledgments) as a postscript file.
 

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FROM THE PREFACE:

As genetic algorithms (GAs) become increasingly popular, they are applied to difficult problems that may require considerable computations. In such cases, parallel implementations of GAs become necessary to reach high-quality solutions in reasonable times. But, even though their mechanics are simple, parallel GAs are complex non-linear algorithms that are controlled by many parameters, which are not well understood.

This book is about the design of parallel GAs. It presents theoretical developments that improve our understanding of the effect of the algorithm's parameters on its search quality and efficiency. These developments are used to formulate guidelines on how to choose the parameter values that minimize the execution time while consistently reaching solutions of high quality.

The book can be read in several ways, depending on the readers' interests and their previous knowledge about these algorithms. Newcomers to the field will find the background material in each chapter useful to become acquainted with previous work, and to understand the problems that must be faced to design efficient and reliable algorithms. Potential users of parallel GAs that may have doubts about their practicality or reliability may be more confident after reading this book and understanding the algorithms better. Those who are ready to try a parallel GA on their applications may choose to skim through the background material, and use the results directly without following the derivations in detail. These readers will find that using the results can help them to choose the type of parallel GA that best suits their needs, without having to invest the time to implement and test various options. Once that is settled, even the most experienced users dread the long and frustrating experience of configuring their algorithms by trial and error. The guidelines contained herein will shorten dramatically the time spent tweaking the algorithm, although some experimentation may still be needed for fine-tuning.

In addition to the results that practitioners will find applicable to the design of parallel GAs, those interested in the mathematical analysis of GAs may find the techniques used here useful for their own endeavors. Indeed, some of the analysis presented here is relevant not only to parallel GAs, but also to their serial counterparts.
 

TABLE OF CONTENTS

Preface
Acknowledgments

1 Introduction
2 The Gambler's Ruin and Population Sizing
3 Master-Slave Parallel GAs
4 Bounding Cases of GAs with Multiple Demes
5 Markov Chain Models of Multiple Demes
6 Migration Rates and Optimal Topologies
7 Migration and Selection Pressure
8 Fine-Grained and Hierarchical Parallel GAs
9 Summary, Extensions, and Conclusions

References
Index