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Abstract: In this paper we present an application of rule-based expert systems to a farming problem. Concretely the prediction of the breeding value in Manchego ewes is studied for the early stage of their life in which the standard (BLUP) methodology cannot be applied. In this case the pedigree index (arithmetical mean between parents’ breeding value) is used to make the estimation. An alternative to this method is presented here, which is based on the use of two different types of rule-based systems: regression rules and linguistic fuzzy rules. The approach proposed is data-driven in the sense that the rules are learnt from data. The results obtained show that the learnt systems are more accurate than the pedigree index, especially for the regression rules case. On the other hand, the linguistic fuzzy rules systems are more easier to understand for human experts, and this is a point to be take into account because of the nature of the problem we are dealing with. [Copyright &y& Elsevier]

Abstract: Estimation of distribution algorithms (EDAs) are a wide-ranging family of evolutionary algorithms whose common feature is the way they evolve by learning a probability distribution from the best individuals in a population and sampling it to generate the next one. Although they have been widely applied to solve combinatorial optimization problems, there are also extensions that work with continuous variables. In this paper [this paper is an extended version of delaOssa et al. Initial approaches to the application of islands-based parellel EDAs in continuous domains, in: Proceedings of the 34th International Conference on Parallel Processing Workshops (ICPP 2005 Workshops), Oslo, 2005, pp. 580–587] we focus on the solution of the latter by means of island models. Besides evaluating the performance of traditional island models when applied to EDAs, our main goal consists in achieving some insight about the behavior and benefits of the migration of probability models that this framework allow. [Copyright &y& Elsevier]