A Study of Crossover Operators for Gene Selection of Microarray Data

Classification of microarray data requires the selection of a subset of relevant genes in order to achieve good classification performance. Several genetic algorithms have been devised to perform this search task. In this paper, we carry out a study on the role of crossover operator and in particular investigate the usefulness of a highly specialized crossover operator called GeSeX (GEne SElection crossover) that takes into account gene ranking information provided by a Support Vector Machine classifier. We present experimental evidences about its performance compared with two other conventional crossover operators. Comparisons are also carried out with several recently reported genetic algorithms on four well-known benchmark data sets.

• Type: Conference
• Year: 2007
• Where: AE
• Authors: Jose Crispin Hernandez Hernandez, Béatrice Duval, Jin-Kao Hao
• Keywords: AE 2007, Artificial Intelligence, Crossover Operators, GEne SElection Crossover, Genetic Algorithms