RT Book, Section
T1 An Accelerated Introduction to Memetic Algorithms.
A1 Moscato, Pablo
A1 Cotta-Porras, Carlos
A2 Gendreau, Michel
A2 Potvin, Jean-Yves
K1 Computación evolutiva
K1 Algoritmos evolutivos
AB 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.
PB Springer
YR 2018
FD 2018
LK https://hdl.handle.net/10630/35055
UL https://hdl.handle.net/10630/35055
LA eng
NO 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
NO Política de acceso abierto tomada de: https://www.springernature.com/gp/open-science/policies/book-policies
NO Australian Research Council grants Future Fellowship FT120100060 and Discovery Project DP140104183. FAPESP, Brazil (1996–2001). Spanish Ministry of Economy and Competitiveness and European Regional Development Fund (FEDER) under project EphemeCH (TIN2014-56494-C4-1-P).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026