RT Journal Article
T1 Harnessing memetic algorithms: a practical guide
A1 Cotta-Porras, Carlos
K1 Algoritmos computacionales
K1 Computación evolutiva
AB 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.
PB Springer Nature
YR 2025
FD 2025-02-03
LK https://hdl.handle.net/10630/37778
UL https://hdl.handle.net/10630/37778
LA eng
NO Cotta, C. (2025). Harnessing memetic algorithms: a practical guide. TOP. https://doi.org/10.1007/s11750-024-00694-8
NO Funding for open access publishing: Universidad de Málaga/CBUA. Carlos Cotta is supported by the Spanish Ministry of Science and Innovation under the Bio4Res project (PID2021-125184NBI00—http://bio4res.lcc.uma.es) and by the Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026