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

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

2011-09-09

arxiv.org/abs/1109.2146v1

Journal Of Artificial Intelligence Research, Volume 24, pages 1-48, 2005

Computer Science

Neural and Evolutionary Computing

Scientific paper

10.1613/jair.1660

In this paper we propose a crossover operator for evolutionary algorithms with real values that is based on the statistical theory of population distributions. The operator is based on the theoretical distribution of the values of the genes of the best individuals in the population. The proposed operator takes into account the localization and dispersion features of the best individuals of the population with the objective that these features would be inherited by the offspring. Our aim is the optimization of the balance between exploration and exploitation in the search process. In order to test the efficiency and robustness of this crossover, we have used a set of functions to be optimized with regard to different criteria, such as, multimodality, separability, regularity and epistasis. With this set of functions we can extract conclusions in function of the problem at hand. We analyze the results using ANOVA and multiple comparison statistical tests. As an example of how our crossover can be used to solve artificial intelligence problems, we have applied the proposed model to the problem of obtaining the weight of each network in a ensemble of neural networks. The results obtained are above the performance of standard methods.

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