Differential Evolution Algorithms for Finding Predictive Gene Subsets in Microarray Data

the selection of gene subsets that retain high predictive accuracy for certain cell-type classification, poses a central problem in microarray data analysis. The application and combination of various computational intelligence methods holds a great promise for automated feature selection and classification. In this paper, we present a new approach based on evolutionary algorithms that addresses the problem of very high dimensionality of the data, by automatically selecting subsets of the most informative genes. The evolutionary algorithm is driven by a neural network classifier. Extensive experiments indicate that the proposed approach is both effective and reliable.