Scalable Optimization via Probabilistic Modeling

  • Martin Pelikan
  • Kumara Sastry
  • Erick CantúPaz

Part of the Studies in Computational Intelligence book series (SCI, volume 33)

Table of contents

  1. Front Matter
    Pages I-XX
  2. Martin Pelikan, Kumara Sastry, Erick Cantú-Paz
    Pages 1-10
  3. Georges R. Harik, Fernando G. Lobo, Kumara Sastry
    Pages 39-61
  4. Martin Pelikan, David E. Goldberg
    Pages 63-90
  5. Yin Shan, Robert I. McKay, Daryl Essam, Hussein A. Abbass
    Pages 121-160
  6. Kumara Sastry, Martin Pelikan, David E. Goldberg
    Pages 161-185
  7. Jiri Ocenasek, Erick Cantú-Paz, Martin Pelikan, Josef Schwarz
    Pages 187-203
  8. Martin Pelikan, Kumara Sastry, David E. Goldberg
    Pages 223-248
  9. Martin Butz, Martin Pelikan, Xavier Llorà, David E. Goldberg
    Pages 249-273
  10. Tian-Li Yu, Scott Santarelli, David E. Goldberg
    Pages 275-289
  11. Jingpeng Li, Uwe Aickelin
    Pages 315-332

About this book


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 of distribution 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


Bayesian Optimization Algorithm Computer-Aided Design (CAD) Distribution Algorithms Evolutionary Algorithms algorithms classification evolutionary algorithm genetic algorithms genetic programming learning machine learning model modeling optimization programming

Editors and affiliations

  • Martin Pelikan
    • 1
  • Kumara Sastry
    • 2
  • Erick CantúPaz
    • 3
  1. 1.Department of Math. And Computer ScienceUniversity of Missouri at St. Louis, One University Blvd.St. LouisUSA
  2. 2.Department of Industrial and Enterprise Systems EngineeringIllinois Genetic Algorithms Laboratory, 414 TBUrbanaUSA
  3. 3.Yahoo! Inc.SunnyvaleUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2006
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-34953-2
  • Online ISBN 978-3-540-34954-9
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site