Real-coded crossover as a role of kernel density estimation

This paper presents a kernel density estimation method by means of real-coded crossovers. Estimation of density algorithms (EDAs) are evolutionary optimization techniques, which determine the sampling strategy by means of a parametric probabilistic density function estimated from the population. Real-coded Genetic Algorithm (RCGA) does not explicitly estimate any probabilistic distribution, however, the probabilistic model of the population is implicitly estimated by crossovers and the sampling strategy is determined by this implicit probabilistic model. Based on this understanding, we propose a novel density estimation algorithm by using crossovers as nonparametric kernels and apply this kernel density estimation to the Gaussian Mixture modeling. We show that the proposed method is superior in the robustness of the computation and in the accuracy of the estimation by the comparison of conventional EM estimation. Categories and Subject Descriptors F.2.1 [Theory of Computation]: Analys...

• Type: Conference
• Year: 2005
• Where: GECCO
• Authors: Jun Sakuma, Shigenobu Kobayashi
• Keywords: Algorithms, Density Estimation Method, GECCO 2005, Kernel Density Estimation