RT Journal Article
T1 A Study on Multimemetic Estimation of Distribution Algorithms
A1 Nogueras, Rafael
A1 Cotta-Porras, Carlos
K1 Resolución de problemas
K1 Algoritmos
AB Multimemetic algorithms (MMAs) are memetic algorithms in which memes (interpreted as non-genetic expressions of problem solvingstrategies) are explicitly represented and evolved alongside genotypes. This process is commonly approached using the standard geneticprocedures of recombination and mutation to manipulate directly information at the memetic level. We consider an alternative approachbased on the use of estimation of distribution algorithms to carry on this self-adaptive memetic optimization process. We study the application ofdifferent EDAs to this end, and provide an extensive experimental evaluation. It is shown that elitism is essential to achieve top performance, and that elitist versions of multimemetic EDAs using bivariate probabilisticmodels are capable of outperforming genetic MMAs.
YR 2014
FD 2014-09-23
LK http://hdl.handle.net/10630/8070
UL http://hdl.handle.net/10630/8070
LA eng
NO PPSN 2014, LNCS 8672, pp. 322-331
NO This work is partially supported by MICINN projectANYSELF (TIN2011-28627-C04-01), by Junta de Andalucía project DNEMESIS (P10-TIC-6083) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026