Evolutionary algorithms for clustering gene-expression data

This work deals with the problem of automatically finding optimal partitions in bioinformatics datasets. We propose incremental improvements for a Clustering Genetic Algorithm (CGA), culminating in the Evolutionary Algorithm for Clustering (EAC). The CGA and its modified versions are evaluated in five gene-expression datasets, showing that the proposed EAC is a promising tool for clustering gene-expression data