Adaptive and Multilevel Metaheuristics

  • Editors
  • Carlos Cotta
  • Marc Sevaux
  • Kenneth Sörensen
Part of the Studies in Computational Intelligence book series (SCI, volume 136)

Table of contents

  1. Front Matter
  2. Reviews of the Field

    1. Front Matter
      Pages 1-1
    2. Konstantin Chakhlevitch, Peter Cowling
      Pages 3-29
  3. New Techniques and Applications

    1. Front Matter
      Pages 59-59
    2. Ignacio Araya, Bertrand Neveu, María-Cristina Riff
      Pages 61-76
    3. Emmanuel Boutillon, Christian Roland, Marc Sevaux
      Pages 77-93
    4. Mauro Brunato, Roberto Battiti
      Pages 95-117
    5. Gustavo Olague, Enrique Dunn, Evelyne Lutton
      Pages 157-176
    6. Roberto Santana, Pedro Larrañaga, José A. Lozano
      Pages 177-197
    7. Alejandro Sierra Urrecho, Iván Santibáñez Koref
      Pages 221-237
    8. Wouter Souffriau, Pieter Vansteenwegen, Greet Vanden Berghe, Dirk Van Oudheusden
      Pages 255-269
  4. Back Matter

About this book

Introduction

One of the keystones in practical metaheuristic problem-solving is the fact that tuning the optimization technique to the problem under consideration is crucial for achieving top performance. This tuning/customization is usually in the hands of the algorithm designer, and despite some methodological attempts, it largely remains a scientific art. Transferring a part of this customization effort to the algorithm itself -endowing it with smart mechanisms to self-adapt to the problem- has been a long pursued goal in the field of metaheuristics.

These mechanisms can involve different aspects of the algorithm, such as for example, self-adjusting the parameters, self-adapting the functioning of internal components, evolving search strategies, etc.

Recently, the idea of hyperheuristics, i.e., using a metaheuristic layer for adapting the search by selectively using different low-level heuristics, has also been gaining popularity. This volume presents recent advances in the area of adaptativeness in metaheuristic optimization, including up-to-date reviews of hyperheuristics and self-adaptation in evolutionary algorithms, as well as cutting edge works on adaptive, self-adaptive and multilevel metaheuristics, with application to both combinatorial and continuous optimization.

Keywords

Layer Problem-solving algorithm algorithms evolution evolutionary algorithm function heuristics metaheuristic optimization particle swarm particle swarm optimization probability search strategy virtual reality

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-79438-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-79437-0
  • Online ISBN 978-3-540-79438-7
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book