This book focuses like a laser beam on one of the hottest topics in
evolutionary computation over the last decade or so: estimation of
distribution algorithms (EDAs). EDAs are an important current
technique that is leading to breakthroughs in genetic and
evolutionary computation and in optimization more generally. I'm
putting Scalable Optimization via Probabilistic Modeling in a
prominent place in my library, and I urge you to do so as well.
This volume summarizes the state of the art at the same time it
points to where that art is going. Buy it, read it, and take its
lessons to heart.
David E Goldberg, University of Illinois at Urbana-Champaign
This book is an excellent compilation of carefully selected
topics in estimation of distribution algorithms---search algorithms
that combine ideas from evolutionary algorithms and machine
learning. The book covers a broad spectrum of important subjects
ranging from design of robust and scalable optimization algorithms
to efficiency enhancements and applications of these algorithms.
The book should be of interest to theoreticians and practitioners
alike, and is a must-have resource for those interested in
stochastic optimization in general, and genetic and evolutionary
algorithms in particular.
John R. Koza, Stanford University
This edited book portrays population-based optimization
algorithms and applications, covering the entire gamut of
optimization problems having single and multiple objectives,
discrete and continuous variables, serial and parallel
computations, and simple and complex function models. Anyone
interested in population-based optimization methods, either
knowingly or unknowingly, use some form of an estimation
ofdistribution algorithm (EDA). This book is an eye-opener and a
must-read text, covering easy-to-read yet erudite articles on
established and emerging EDA methodologies from real experts in the
field.
Kalyanmoy Deb, Indian Institute of Technology Kanpur
This book is an excellent comprehensive resource on estimation
of distribution algorithms. It can serve as the primary EDA
resource for practitioner or researcher. The book includes chapters
from all major contributors to EDA state-of-the-art and covers the
spectrum from EDA design to applications. These algorithms
strategically combine the advantages of genetic and evolutionary
computation with the advantages of statistical, model building
machine learning techniques. EDAs are useful to solve classes of
difficult real-world problems in a robust and scalable manner.
Una-May O'Reilly, Massachusetts Institute of Technology
Machine-learning methods continue to stir the public's
imagination due to its futuristic implications. But,
probability-based optimization methods can have great impact now on
many scientific multiscale and engineering design problems,
especially true with use of efficient and competent genetic
algorithms (GA) which are the basis of the present volume. Even
though efficient and competent GAs outperform standard techniques
and prevent negative issues, such as solution stagnation, inherent
in the older but more well-known GAs, they remain less known or
embraced in the scientific and engineering communities. To that
end, the editors have brought together a selection of experts that
(1) introduce the current methodology and lexicography of the field
with illustrative discussions and highly useful references,
(2)exemplify these new techniques that dramatic improve performance
in provable hard problems, and (3) provide real-world applications
of these techniques, such as antenna design. As one who has strayed
into the use of genetic algorithms and genetic programming for
multiscale modeling in materials science, I can say it would have
been personally more useful if this would have come out five years
ago, but, for my students, it will be a boon.
Duane D. Johnson, University of Illinois at Urbana-Champaign
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