Two evolutionary approaches to cross-clustering problems

Cross-clustering asks for a Boolean matrix to be brought to a quasi-canonical form. The problem has many applications in image processing, circuit design, archaeology, ecology etc. The heuristics currently used to solve it rely on either topological sorting or quasi-random search. We present here two evolutionary approaches to this problem: a permutation-based solution and a clustering one. The results on both real data and randomly generated, scalable, test data show very good convergence and encouraging efficiency properties, mainly for our second approach.