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An Accelerated Introduction to Memetic Algorithms. Moscato, Pablo Cotta-Porras, Carlos Gendreau, Michel Potvin, Jean-Yves Computación evolutiva Algoritmos evolutivos Política de acceso abierto tomada de: https://www.springernature.com/gp/open-science/policies/book-policies 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. 2024-11-07T11:51:58Z 2024-11-07T11:51:58Z 2018 book part 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 https://hdl.handle.net/10630/35055 10.1007/978-3-319-91086-4_9 eng open access Springer