C. J. Carmona, P. González, M. J. Jesús, F. Herrera
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引用次数: 1
Abstract
A main purpose of a multi-objective evolutionary algorithm is to find a good relationship between convergence and diversity of the population. Convergence guides the algorithm to search the optimal solution and diversity tries to avoid a premature stagnation of the search. In multi-objective evolutionary algorithms, diversity has been promoted using different techniques. In this paper, several diversity functions were implemented in NMEEF-SD, an algorithm for the extraction of fuzzy rules in a subgroup discovery task, to analyse the influence of these functions in the evolutionary process. The results show the advantages of the different measures, depending on the intended objective.
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