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

TL;DR

This paper introduces CIXL2, a novel crossover operator for real-valued evolutionary algorithms that leverages population distribution statistics to improve search efficiency and robustness across diverse optimization problems.

Contribution

The paper presents a new crossover operator based on population feature distributions, enhancing exploration and exploitation in evolutionary algorithms.

Findings

CIXL2 outperforms standard crossover methods on various benchmark functions.

The operator effectively balances exploration and exploitation.

Application to neural network ensemble weighting yields superior results.

Abstract

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.