A study of crossover operators for gene selection of microarray data

Authors:
Jose Crispin Hernandez Hernandez;Béatrice Duval;Jin-Kao Hao
Affiliations:
LERIA, Université d'Angers, Angers, France;LERIA, Université d'Angers, Angers, France;LERIA, Université d'Angers, Angers, France
Venue:
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
Year:
2007

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Abstract

Classification of microarray data requires the selection ofa subset of relevant genes in order to achieve good classification performance.Several genetic algorithms have been devised to perform thissearch task. In this paper, we carry out a study on the role of crossover operatorand in particular investigate the usefulness of a highly specializedcrossover operator called GeSeX (GEne SElection crossover) that takesinto account gene ranking information provided by a Support Vector Machineclassifier. We present experimental evidences about its performancecompared with two other conventional crossover operators. Comparisonsare also carried out with several recently reported genetic algorithms onfour well-known benchmark data sets.