Memetic Algorithms and Complete Techniques

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.

Authors and Affiliations

1. Dept. Lenguajes y Ciencias de la Computación, Universidad de Málaga, ETSI Informática, Campus de Teatinos, 29071, Málaga, Spain

   Carlos Cotta, Antonio J. Fernández Leiva & José E. Gallardo

Editors and Affiliations

1. Dept. of Mathematical Information Technology, University of Jyväskylä, P.O. Box 35, FI-40014, Jyväskylä, Finland

   Ferrante Neri

2. Escuela Técnica Superior de Ingeniería Informática, Universidad Málaga, Campus de Teatinos, s/n, 29071, Málaga, Spain

   Carlos Cotta

3. School of Electrical Engineering & Computer Science, University of Newcastle, University Drive, 2308, Callaghan, NSW, Australia

   Pablo Moscato

Reprints and permissions

© 2012 Springer-Verlag Berlin Heidelberg

Policies and ethics