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An Accelerated Introduction to Memetic Algorithms

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 272))

Abstract

Memetic algorithms (MAs) are optimization techniques based on the orchestrated interplay between global and local search components and have the exploitation of specific problem knowledge as one of their guiding principles. In its most classical form, a MA is typically composed of an underlying population-based engine onto which a local search component is integrated. These aspects are described in this chapter in some detail, paying particular attention to design and integration issues. After this description of the basic architecture of MAs, we move to different algorithmic extensions that give rise to more sophisticated memetic approaches. After providing a meta-review of the numerous practical applications of MAs, we close this chapter with an overview of current perspectives of memetic algorithms.

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Acknowledgements

This chapter is an update of [122], refurbished with new references and the inclusion of sections on timely topics which were not fully addressed in the previous editions. Pablo Moscato acknowledges funding of his research by the Australian Research Council grants Future Fellowship FT120100060 and Discovery Project DP140104183. He also acknowledges previous support by FAPESP, Brazil (1996–2001). Carlos Cotta acknowledges the support of Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-1-P).

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    Pablo Moscato

  2. Escuela Técnica Superior de Ingeniería Informática, Universidad de Málaga, Málaga, Spain

    Carlos Cotta

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Moscato, P., Cotta, C. (2019). An Accelerated Introduction to Memetic Algorithms. In: Gendreau, M., Potvin, JY. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol 272. Springer, Cham. https://doi.org/10.1007/978-3-319-91086-4_9

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