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An Accelerated Introduction to Memetic Algorithms

In: Handbook of Metaheuristics

Author

Listed:
  • Pablo Moscato

    (The University of Newcastle)

  • Carlos Cotta

    (Universidad de Málaga)

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.

Suggested Citation

  • Pablo Moscato & Carlos Cotta, 2019. "An Accelerated Introduction to Memetic Algorithms," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, edition 3, chapter 0, pages 275-309, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-91086-4_9
    DOI: 10.1007/978-3-319-91086-4_9
    as

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