Real-coded Crossovers as a Role of Kernel Density Estimator

Proposal of Crossover Kernels based on Unimordal Normal Distribution Crossover

Abstract

This paper presents a kernel density estimation method by means of real-coded crossovers. Functions of real-coded crossover operators are composed of probabilistic density estimation from parental populations and sampling from estimated models. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate probabilistic distributions, however, probabilistic model estimation is implicitly included in algorithms of real-coded crossovers. Based on this understanding, we exploit the implicit estimation of probabilistic distribution of crossovers as a kernel density estimator. We also propose an application of crossover kernels to Expectation-Maximization estimation (EM) of Gaussian mixtures.