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.