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In this paper we propose a crossover operator for evolutionary algorithmswith real values that is based on the statistical theory of populationdistributions. The operator is based on the theoretical distribution of thevalues of the genes of the best individuals in the population. The proposedoperator takes into account the localization and dispersion features of thebest individuals of the population with the objective that these features wouldbe inherited by the offspring. Our aim is the optimization of the balancebetween exploration and exploitation in the search process. In order to testthe efficiency and robustness of this crossover, we have used a set offunctions to be optimized with regard to different criteria, such as,multimodality, separability, regularity and epistasis. With this set offunctions we can extract conclusions in function of the problem at hand. Weanalyze the results using ANOVA and multiple comparison statistical tests. Asan example of how our crossover can be used to solve artificial intelligenceproblems, we have applied the proposed model to the problem of obtaining theweight of each network in a ensemble of neural networks. The results obtainedare above the performance of standard methods.

CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features