Book announcement: Estimation of Distribution Algorithms

[Larranaga Mugica Pedro <ccplamup@si.ehu.es>]

I want to inform the scheduling community of a new book

Estimation of Distribution Algorithms. A new Tool for Evolutionary
Computation.

Edited by P. Larraņaga and Jose A. Lozano, Kluwer Academic Publishers, 2002.

For more detailed information, table of contents, abstracts
and chapters see:

http://www.sc.ehu.es/isg
http://www.wkap.nl/prod/s/GENA

Summary:

The book is devoted to a new paradigm
for Evolutionary
Computation, named Estimation of Distribution Algorithms (EDAs).
Based on Genetic Algorithms (GAs), this new class of algorithms
generalizes GAs by replacing the crossover and mutation operators
by  learning and sampling the probability distribution of the best
individuals of the population at each iteration of the algorithm.
Working in such a way, the relationships between the variables
involved in the problem domain are explicitly and effectively
captured and exploited.

This text constitutes the first compilation and review of the
techniques and applications of this new tool for performing
Evolutionary Computation. The book is clearly divided into three parts and
comprised of a total of 18 chapters.

Part I is dedicated to the foundations of EDAs. In this part
different paradigms for Evolutionary Computation are introduced
and  some probabilistic graphical models --Bayesian networks and
Gaussian networks-- used in  learning and sampling  the
probability distribution of the selected individuals at each
iteration of EDAs are presented. In addition to this, a review of
the existing EDA approaches is carried out. Also EDAs based on the
learning  mixture models are presented and some approaches to the
parallelization of the learning task are introduced. This part
concludes with the mathematical modeling of some of the proposed
EDA approaches.


Part II brings together several applications of EDAs in
optimization problems and reports on the results reached. Among
the solved problems are the following ones: the traveling salesman
problem, the job scheduling problem and the knapsack problem, as
well as the optimization of some well-known combinatorial and
continuous  functions. This part ends  with a chapter devoted to
an EDA based approach to the inexact graph matching problem.


Part III presents the application of EDAs in order to solve some
problems that arise in the Machine Learning field. Concretely, the
problems considered are: feature subset selection, feature
weigthing in K-NN classifiers, rule induction, partial abductive
inference in Bayesian networks, partitional clustering and the
searching for optimal weights in artificial neural networks.

This book can be a useful and interesting tool  for
researchers working in the field of Evolutionary Computation. Also  engineers who, in their every day
life, face real-world optimization problems and whom are provided with
a new and powerful tool can derive benefit from the reading of the book. Moreover, this book may be used by graduate
students in computer science and by people interested in taking part
in the development of this new methodology that, in the following
years, will provide us with interesting and appealing challenges.




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Pedro Larranaga

Dept. of Computer Science and Artificial Intelligence
University of the Basque Country
M. de Lardizabal Pasalekua, 1
20009 Donostia-San Sebastian
Spain

e-mail: ccplamup@si.ehu.es
Phone   +34 943 018045
Fax     +34 943 219306
http://www.sc.ehu.es/isg
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