Publications

Dear colleagues,

I am very pleased to announce the publication of my book
"Efficient and Accurate Parallel Genetic Algorithms" by
Kluwer Academic Publishers.

http://www.wkap.nl/book.htm/0-7923-7221-2

This is the first volume in Kluwer's series on Genetic Algorithms
and Evolutionary Computation. The book presents recent research
results that aid in the design of different types of parallel GAs to
find good solutions efficiently. I tried to include material to
make the book appealing to researchers as well as to novice and
seasoned practitioners.

I hope that you will find the book useful, and I welcome your
comments.

Sincerely,
Erick Cantu-Paz

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