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© 2012

Markov Networks in Evolutionary Computation

  • Siddhartha Shakya
  • Roberto Santana
  • Offers a systematic presentation of the use of Markov Networks in Evolutionary Computation

  • Fills a void in the current literature on the application of PGMs in evolutionary optimization

  • Written by leading experts in the field

Book

Part of the Adaptation, Learning, and Optimization book series (ALO, volume 14)

Table of contents

  1. Front Matter
    Pages 1-17
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Roberto Santana, Siddhartha Shakya
      Pages 3-19
    3. Siddhartha Shakya, Roberto Santana
      Pages 21-37
    4. Siddhartha Shakya, Roberto Santana
      Pages 39-53
    5. Siddhartha Shakya, John McCall, Alexander Brownlee, Gilbert Owusu
      Pages 55-71
  3. Theory

    1. Front Matter
      Pages 89-89
    2. Alexander E. I. Brownlee, John A. W. McCall, Siddhartha K. Shakya
      Pages 125-140
    3. Alexander Mendiburu, Roberto Santana, Jose A. Lozano
      Pages 141-155
    4. Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga
      Pages 157-173
  4. Application

    1. Front Matter
      Pages 191-191
    2. John McCall, Alexander Brownlee, Siddhartha Shakya
      Pages 193-207
    3. Marta Soto, Alberto Ochoa, Yasser González-Fernández, Yanely Milanés, Adriel Álvarez, Diana Carrera et al.
      Pages 209-225
  5. Back Matter
    Pages 0--1

About this book

Introduction

Markov networks and other probabilistic graphical modes have recently received an upsurge in attention from Evolutionary computation community, particularly in the area of Estimation of distribution algorithms (EDAs).  EDAs have arisen as one of the most successful experiences in the application of machine learning methods in optimization, mainly due to their efficiency to solve complex real-world optimization problems and their suitability for theoretical analysis.

This book focuses on the different steps involved in the conception, implementation and application of EDAs that use Markov networks, and undirected models in general. It can serve as a general introduction to EDAs but covers also an important current void in the study of these algorithms by explaining the specificities and benefits of modeling optimization problems by means of undirected probabilistic models.

All major developments to date in the progressive introduction of Markov networks based EDAs are reviewed in the book. Hot current research trends and future perspectives in the enhancement and applicability of EDAs are also covered.  The contributions included in the book address topics as relevant as the application of probabilistic-based fitness models, the use of belief propagation algorithms in EDAs and the application of Markov network based EDAs to real-world optimization problems. The book should be of interest to researchers and practitioners from areas such as optimization, evolutionary computation, and machine learning.

Keywords

Estimation of Distribution Algorithms Evolutionary Algorithms Graphical Models Markov Models Metaheuristics Optimization

Editors and affiliations

  • Siddhartha Shakya
    • 1
  • Roberto Santana
    • 2
  1. 1.Transformation Practice, BT Innovate & DesignBusiness Modelling and OperationalIpswichUnited Kingdom
  2. 2.Intelligent Systems Group, Faculty of InformaticsUniversity of the Basque CountrySan SebastianSpain

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