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A Modern Introduction to Memetic Algorithms

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Handbook of Metaheuristics

Part of the book series: International Series in Operations Research & Management Science ((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.

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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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  1. Centre for Bioinformatics, Biomarker Discovery and Information-based Medicine, The University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia

    Pablo Moscato

  2. Departamento de Lenguajes y Ciencias de la Computación, Escuela Técnica Superior de Ingeniería Informática, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

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    Michel Gendreau

  2. Dépt. Informatique et Recherche, Opérationnelle, Université de Montreal, Pavillon André-Aisenstadt, Montreal, H3C 3J7, Québec, Canada

    Jean-Yves Potvin

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Moscato, P., Cotta, C. (2010). A Modern Introduction to Memetic Algorithms. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 146. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1665-5_6

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