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A Modern Introduction to Memetic Algorithms

  • Pablo Moscato
  • Carlos Cotta
Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 146)

Abstract

Memetic algorithms are optimization techniques based on the synergistic combination of ideas taken from different algorithmic solvers, such as population-based search (as in evolutionary techniques) and local search (as in gradient-ascent techniques). After providing some historical notes on the origins of memetic algorithms, this work shows the general structure of these techniques, including some guidelines for their design. Some advanced topics such as multiobjective optimization, self-adaptation, and hybridization with complete techniques (e.g., branch-and-bound) are subsequently addressed. This chapter finishes with an overview of the numerous applications of these techniques and a sketch of the current development trends in this area.

Keywords

Local Search Combinatorial Optimization Problem Vertex Cover Memetic Algorithm Fitness Landscape 
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.

Notes

Acknowledgments

This chapter is an updated second edition of [180], refurbished with new references and the inclusion of sections on timely topics which were not fully addressed in the first edition. Carlos Cotta acknowledges the support of Spanish Ministry of Science and Innovation, under project TIN2008-05941.

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© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Centre for Bioinformatics, Biomarker Discovery and Information-based MedicineThe University of NewcastleCallaghanAustralia
  2. 2.Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería InformáticaUniversidad de MálagaMálagaSpain

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