Absfract-In recent years, several hiclustering methods have been suggested to identify l&al patterns in gene expression data. Most of these algorithms represent greedy strategies that are heuristic in nature: an approximate solution is found within reasonable time hounds. The quality of a biclustering, though, is often considered more important than the computation time required to generate it. Therefore, this paper addresses the question whether additional run-time resources can be exploited in order to improve the outcome of the aforementioned greedy algorithms. To this end, we propose a general framework that embeds such hiclustering methods as local search procedures in an evolutionary algorithm. We demonstrate on one prominent example that this approach achieves significant improvements in the quality of the biclusters when compared to the application of the greedy strategy alone.
Bleuler et al. REF propose a evolutionary algorithm framework that embeds a greedy strategy.
An EA framework for biclustering of gene expression data
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753)
Computer Science