Evolving MNK-landscapes with Structural Constraints


In this paper we propose a method for the generation of instances of the MNK-landscapes that maximize different measures used to characterize multi-objective problems. In contrast to previous approaches, the introduced algorithm works by modifying the neighborhood structure of the variables of the MNK-landscape while keeping fixed the local parameters of its functions. A variant of the algorithm is presented to deal with situations in which the exhaustive enumeration of search space is unfeasible. We show how the introduced method can be used to generate instances with an increased number of solutions in the Pareto front. Furthermore, we investigate whether direct optimization of the correlation between objectives can be used as an indirect method to increase the size of the Pareto fronts of the generated instances.