Evolution strategy applied to global optimization of clusters in gene expression data of DNA microarrays

Cluster analysis is the most important method for analyzing large-scale gene expression patterns. The matrix representation of microarray data and its successive `optimal´ incisional hyperplanes that create top-down hierarchical tree are a useful platform for developing optimization algorithms to determine the `optimal´ clusters from a pairwise proximity matrix which represents completely connected and weighted graph. Evolution strategy is applied to determine the `globally optimal´ incisional hyperplanes to construct hierarchical tree structure and tested with Fisher´s iris and Golub´s leukemia data sets. The results were compared with those of bottom-up hierarchical clustering, K-means and SOMs (Self-Organizing Maps) algorithms with promising results