Improving model combination through local search in parallel univariate EDAs

Migration of probabilistic models instead of individuals has been shown beneficial in islands-based models of parallel univariate estimation of distribution algorithms (EDAs). One of the key points when using this type of migration is how to incorporate the incoming probabilistic model to the inner one in a given island. When dealing with combinatorial optimization problems and univariate EDAs, models can be combined successfully by using a convex combination of the two probabilistic models (delaOssa et al., 2004). In this paper, we present an alternative way of combining probabilistic models. The new proposal for model combination is based on local search methods, and has its motivation in trying to identify what parts of the incoming model can help to improve the inner one, and to use only these parts to update the incoming model, instead of updating the whole one. Several algorithms are proposed and evaluated by using different test problems. The experiments show that the new proposals perform better than those based on convex combination, especially in the most difficult test problems.