An incomplete dominance genetic algorithm approach to microarray data analysis

We address the problem of analyzing the vast amount of data involved in microarray studies. The finality is to discover, from a large pool of candidates, a limited number of genes that could be causally related with a specific pathology. In this context, we propose a new genetic algorithm (GA) approach for feature selection, with diploid number of chromosomes and an incomplete dominance model for genotype to phenotype mapping. We test our algorithm on a familiar data set for performance evaluation purposes.