Dependency structure matrix, genetic algorithms and effective recombination, Evolutionary Computation 17(4

In many different fields, researchers are often confronted by problems arising from complex systems. Simple heuristics or even enumeration works quite well on small and easy problems; however, to efficiently solve large and difficult problems, proper decomposition is the key. In this paper, investigating and analyzing interactions be-tween components of complex systems shed some light on problem decomposition. By recognizing three bare-bones interactions—modularity, hierarchy, and overlap, facet-wisemodels are developed to dissect and inspect problemdecomposition in the context of genetic algorithms. The proposed genetic algorithm design utilizes a matrix repre-sentation of an interaction graph to analyze and explicitly decompose the problem. The results from this paper should benefit research both technically and scientifically. Technically, this paper develops an automated dependency structure matrix clustering technique and utilizes it to design a model-building genetic algorithm that learns and delivers the problem structure. Scientifically, the explicit interaction model describes the problem structure very well and helps researchers gain important insights through the explicitness of the procedure