DocumentCode :
2212540
Title :
Analysis of the impact of using different diversity functions for the subgroup discovery algorithm NMEEF-SD
Author :
Carmona, Cristóbal J. ; González, Pedro ; del Jesus, Maria José ; Herrera, Francisco
Author_Institution :
Dept. of Comput. Sci., Univ. of Jaen, Jaen, Spain
fYear :
2011
fDate :
11-15 April 2011
Firstpage :
17
Lastpage :
23
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.
Keywords :
data mining; evolutionary computation; fuzzy systems; NMEEF-SD; fuzzy rules; multiobjective evolutionary algorithm; subgroup discovery algorithm; Algorithm design and analysis; Atmospheric measurements; Convergence; Data mining; Evolutionary computation; Particle measurements; Pragmatics; Evolutionary Fuzzy System; NMEEF-SD; NSGA-II; Subgroup Discovery;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Genetic and Evolutionary Fuzzy Systems (GEFS), 2011 IEEE 5th International Workshop on
Conference_Location :
Paris
Print_ISBN :
978-1-61284-049-9
Type :
conf
DOI :
10.1109/GEFS.2011.5949498
Filename :
5949498
Link To Document :
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