Recent Advances in Evolutionary Computation for Combinatorial Optimization

  • Editors
  • Carlos Cotta
  • Jano van Hemert
Part of the Studies in Computational Intelligence book series (SCI, volume 153)

Table of contents

  1. Front Matter
  2. Theory and Methodology

  3. Hybrid Approaches

    1. Front Matter
      Pages 67-67
    2. Napoleão Nepomuceno, Plácido Pinheiro, André L. V. Coelho
      Pages 87-99
    3. Enrique Alba, Gabriel Luque
      Pages 101-112
    4. Ferrante Neri, Niko Kotilainen, Mikko Vapa
      Pages 113-129
  4. Constrained Problems

    1. Front Matter
      Pages 131-131
    2. Francisco Luna, Enrique Alba, Antonio J. Nebro, Salvador Pedraza
      Pages 151-166
  5. Scheduling

  6. Routing and Travelling Salesman Problems

    1. Front Matter
      Pages 241-241

About this book

Introduction

Combinatorial optimisation is a ubiquitous discipline whose usefulness spans vast applications domains. The intrinsic complexity of most combinatorial optimisation problems makes classical methods unaffordable in many cases. To acquire practical solutions to these problems requires the use of metaheuristic approaches that trade completeness for pragmatic effectiveness. Such approaches are able to provide optimal or quasi-optimal solutions to a plethora of difficult combinatorial optimisation problems.

The application of metaheuristics to combinatorial optimisation is an active field in which new theoretical developments, new algorithmic models, and new application areas are continuously emerging. This volume presents recent advances in the area of metaheuristic
combinatorial optimisation, with a special focus on evolutionary computation methods. Moreover, it addresses local search methods and hybrid approaches. In this sense, the book includes cutting-edge theoretical, methodological, algorithmic and applied developments in the field, from respected experts and with a sound perspective.

Keywords

Permutation Scala algorithm algorithms combinatorial optimization complexity evolutionary algorithm genetic algorithms metaheuristic optimization scheduling

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-70807-0
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-70806-3
  • Online ISBN 978-3-540-70807-0
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503