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Handbook of Metaheuristics

  • Fred Glover
  • Gary A. Kochenberger

Part of the International Series in Operations Research & Management Science book series (ISOR, volume 57)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Fred Glover, Manuel Laguna, Rafael Marti
    Pages 1-35
  3. Michel Gendreau
    Pages 37-54
  4. Colin Reeves
    Pages 55-82
  5. Pablo Moscato, Carlos Cotta
    Pages 105-144
  6. Pierre Hansen, Nenad Mladenović
    Pages 145-184
  7. Christos Voudouris, Edward P. K. Tsang
    Pages 185-218
  8. Mauricio G. C. Resende, Celso C. Ribeiro
    Pages 219-249
  9. Darrall Henderson, Sheldon H. Jacobson, Alan W. Johnson
    Pages 287-319
  10. Helena R. Lourenço, Olivier C. Martin, Thomas Stützle
    Pages 320-353
  11. Rafael Martí
    Pages 355-368
  12. Filippo Focacci, François Laburthe, Andrea Lodi
    Pages 369-403
  13. Eugene C. Freuder, Mark Wallace
    Pages 405-428
  14. Jean-Yves Potvin, Kate A. Smith
    Pages 429-455
  15. Edmund Burke, Graham Kendall, Jim Newall, Emma Hart, Peter Ross, Sonia Schulenburg
    Pages 457-474
  16. Teodor Gabriel Crainic, Michel Toulouse
    Pages 475-513
  17. Andreas Fink, Stefan Voß, David L. Woodruff
    Pages 515-535
  18. Sarosh Talukdar, Sesh Murthy, Rama Akkiraju
    Pages 537-556
  19. Back Matter
    Pages 557-557

About this book

Introduction

Metaheuristics, in their original definition, are solution methods that orchestrate an interaction between local improvement procedures and higher level strategies to create a process capable of escaping from local optima and performing a robust search of a solution space. Over time, these methods have also come to include any procedures that employ strategies for overcoming the trap of local optimality in complex solution spaces, especially those procedures that utilize one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes. The degree to which neighborhoods are exploited varies according to the type of procedure. In the case of certain population-based procedures, such as genetic al- rithms, neighborhoods are implicitly (and somewhat restrictively) defined by reference to replacing components of one solution with those of another, by variously chosen rules of exchange popularly given the name of “crossover. ” In other population-based methods, based on the notion of path relinking, neighborhood structures are used in their full generality, including constructive and destructive neighborhoods as well as those for transitioning between (complete) solutions. Certain hybrids of classical evoluti- ary approaches, which link them with local search, also use neighborhood structures more fully, though apart from the combination process itself.

Keywords

Constraint satisfaction algorithms combinatorial optimization genetic algorithms genetic programming heuristics metaheuristic optimization programming

Editors and affiliations

  • Fred Glover
    • 1
  • Gary A. Kochenberger
    • 2
  1. 1.Leeds School of BusinessUniversity of Colorado at BoulderUSA
  2. 2.College of BusinessUniversity of Colorado at DenverUSA

Bibliographic information

  • DOI https://doi.org/10.1007/b101874
  • Copyright Information Kluwer Academic Publishers 2003
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4020-7263-5
  • Online ISBN 978-0-306-48056-0
  • Series Print ISSN 0884-8289
  • Buy this book on publisher's site