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oai:riuma.uma.es:10630/377782026-02-03T11:06:29Zcom_10630_2254col_10630_37953

Harnessing memetic algorithms: a practical guide Cotta-Porras, Carlos Algoritmos computacionales Computación evolutiva The aim of this work is to provide a didactic approximation to memetic algorithms (MAs) and how to apply these techniques to an optimization problem. MAs are based on the synergistic combination of ideas from population-based metaheuristics and trajectory-based search/optimization techniques. Most commonly, MAs feature a population-based algorithm as the underlying search engine, endowing it with problem-specific components for exploring the search space, and in particular with local-search mechanisms. In this work, we describe the design of the different elements of the MA to fit the problem under consideration, and go on to perform a detailed case study on a constrained combinatorial optimization problem related to aircraft landing scheduling. An outline of some advanced topics and research directions is also provided. 2025-02-11T10:40:56Z 2025-02-11T10:40:56Z 2025-02-03 journal article Cotta, C. (2025). Harnessing memetic algorithms: a practical guide. TOP. https://doi.org/10.1007/s11750-024-00694-8 https://hdl.handle.net/10630/37778 10.1007/s11750-024-00694-8 eng http://creativecommons.org/licenses/by/4.0/ open access Atribución 4.0 Internacional Springer Nature