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oai:riuma.uma.es:10630/80702026-02-03T11:30:14Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Nogueras, Rafael author Cotta-Porras, Carlos author 2014-09-23 Multimemetic algorithms (MMAs) are memetic algorithms in which memes (interpreted as non-genetic expressions of problem solving strategies) are explicitly represented and evolved alongside genotypes. This process is commonly approached using the standard genetic procedures of recombination and mutation to manipulate directly information at the memetic level. We consider an alternative approach based on the use of estimation of distribution algorithms to carry on this self-adaptive memetic optimization process. We study the application of different 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 probabilistic models are capable of outperforming genetic MMAs. http://hdl.handle.net/10630/8070 Resolución de problemas Algoritmos A Study on Multimemetic Estimation of Distribution Algorithms