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Memetic Algorithms and Complete Techniques

  • Carlos Cotta
  • Antonio J. Fernández Leiva
  • José E. Gallardo
Part of the Studies in Computational Intelligence book series (SCI, volume 379)

Introduction

As mentioned in previous chapters in this volume, metaheuristics (and specifically MAs) have a part of their raison d’etre in practically solving problems whose resolution would be otherwise infeasible by means of other non-heuristic approaches. Such alternative non-heuristic approaches are complete methods that –unlike heuristics– do guarantee that the deviation from optimality of the solution they will provide is somehow bounded (and as a particular case, that the optimal solution will be found). These methods are eventually limited by the curse of dimensionality, yet they may still constitute a very interesting resource either from the application point of view, or from the lessons that can be learnt from them. Indeed, in some sense these approaches could be considered complementary to metaheuristics rather that mere “rivals”. Even more so in the case of MAs, whose philosophy has been since its inception much more flexible and integrative rather than dogmatic or exclusive.

Keywords

Local Search Search Tree Hybrid Algorithm Memetic Algorithm Beam Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carlos Cotta
    • 1
  • Antonio J. Fernández Leiva
    • 1
  • José E. Gallardo
    • 1
  1. 1.Dept. Lenguajes y Ciencias de la ComputaciónUniversidad de Málaga, ETSI InformáticaMálagaSpain

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