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In Tri-clustering, a sub-matrix is being created, which exhibit highlysimilar behavior with respect to genes, conditions and time-points. In thistechnique, genes with same expression values are discovered across somefragment of time points, under certain conditions. In this paper, triclusteringusing evolutionary algorithm is implemented using a new fitness functionconsisting of 3D Mean Square residue (MSR) and Least Square approximation(LSL). The primary objective is to find triclusters with minimum overlapping,low MSR, low LSL and covering almost every element of expression matrix, thusminimizing the overall fitness value. To improve the results of algorithm, newfitness function is introduced to find good quality triclusters. It is observedfrom experiments that, triclustering using EA yielded good quality triclusters.The experiment was implemented on yeast Saccharomyces dataset. IndexTerms-Tri-clustering, Genetic Algorithm, Mean squared residue, Volume, Weights,Least square approximation.

Triclustering of Gene Expression Microarray data using Evolutionary Approach