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Scalable Optimization via Probabilistic Modeling

From Algorithms to Applications

  • Book
  • © 2006

Overview

Editors:
  1. Martin Pelikan

    1. Department of Math. And Computer Science, University of Missouri at St. Louis, One University Blvd., St. Louis, USA

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  2. Kumara Sastry

    1. Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Laboratory, 414 TB, Urbana, USA

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    You can also search for this editor in PubMed   Google Scholar

  3. Erick CantúPaz

    1. Yahoo! Inc., Sunnyvale, USA

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    You can also search for this editor in PubMed   Google Scholar

  • one of the hottest topics in evolutionary computation
  • excellent compilation of carefully selected topics in estimation of distribution algorithms---search algorithms that combine ideas from evolutionary algorithms and machine learning.
  • 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.
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XX
    Download chapter PDF

  2. Introduction

    • Martin Pelikan, Kumara Sastry, Erick Cantú-Paz
    Pages 1-10

  3. The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle

    • Heinz Mühlenbein, Robin Höns
    Pages 11-37

  4. Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)

    • Georges R. Harik, Fernando G. Lobo, Kumara Sastry
    Pages 39-61

  5. Hierarchical Bayesian Optimization Algorithm

    • Martin Pelikan, David E. Goldberg
    Pages 63-90

  6. Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms

    • Peter A. N. Bosman, Dirk Thierens
    Pages 91-120

  7. A Survey of Probabilistic Model Building Genetic Programming

    • Yin Shan, Robert I. McKay, Daryl Essam, Hussein A. Abbass
    Pages 121-160

  8. Efficiency Enhancement of Estimation of Distribution Algorithms

    • Kumara Sastry, Martin Pelikan, David E. Goldberg
    Pages 161-185

  9. Design of Parallel Estimation of Distribution Algorithms

    • Jiri Ocenasek, Erick Cantú-Paz, Martin Pelikan, Josef Schwarz
    Pages 187-203

  10. Incorporating a priori Knowledge in Probabilistic-Model Based Optimization

    • Shumeet Baluja
    Pages 205-222

  11. Multiobjective Estimation of Distribution Algorithms

    • Martin Pelikan, Kumara Sastry, David E. Goldberg
    Pages 223-248

  12. Effective and Reliable Online Classification Combining XCS with EDA Mechanisms

    • Martin Butz, Martin Pelikan, Xavier Llorà, David E. Goldberg
    Pages 249-273

  13. Military Antenna Design Using a Simple Genetic Algorithm and hBOA

    • Tian-Li Yu, Scott Santarelli, David E. Goldberg
    Pages 275-289

  14. Feature Subset Selection with Hybrids of Filters and Evolutionary Algorithms

    • Erick Cantú-Paz
    Pages 291-314

  15. BOA for Nurse Scheduling

    • Jingpeng Li, Uwe Aickelin
    Pages 315-332

  16. Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation

    • Martin Pelikan, Alexander K. Hartmann
    Pages 333-349
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Keywords

About this book

I’m not usually a fan of edited volumes. Too often they are an incoherent hodgepodge of remnants, renegades, or rejects foisted upon an unsuspecting reading public under a misleading or fraudulent title. The volume Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications is a worthy addition to your library because it succeeds on exactly those dimensions where so many edited volumes fail. For example, take the title, Scalable Optimization via Probabilistic M- eling: From Algorithms to Applications. You need not worry that you’re going to pick up this book and ?nd stray articles about anything else. This book focuseslikealaserbeamononeofthehottesttopicsinevolutionary compu- tion over the last decade or so: estimation of distribution algorithms (EDAs). EDAs borrow evolutionary computation’s population orientation and sel- tionism and throw out the genetics to give us a hybrid of substantial power, elegance, and extensibility. The article sequencing in most edited volumes is hard to understand, but from the get go the editors of this volume have assembled a set of articles sequenced in a logical fashion. The book moves from design to e?ciency enhancement and then concludes with relevant applications. The emphasis on e?ciency enhancement is particularly important, because the data-mining perspectiveimplicitinEDAsopensuptheworldofoptimizationtonewme- ods of data-guided adaptation that can further speed solutions through the construction and utilization of e?ective surrogates, hybrids, and parallel and temporal decompositions.

Editors and Affiliations

  • Department of Math. And Computer Science, University of Missouri at St. Louis, One University Blvd., St. Louis, USA

    Martin Pelikan

  • Department of Industrial and Enterprise Systems Engineering, Illinois Genetic Algorithms Laboratory, 414 TB, Urbana, USA

    Kumara Sastry

  • Yahoo! Inc., Sunnyvale, USA

    Erick CantúPaz

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