Density estimation using crossover kernels and its application to a real-coded genetic algorithm

Sakuma and Kobayashi have proposed a density estimation method that utilizes real-coded crossover operators. However, their method was used only to estimate normal distribution functions. In order to estimate more complicated PDFs, this study proposes a new density estimation method of utilizing crossover operators. When we try to solve function optimization problems, on the other hand, real-coded genetic algorithms (GAs) show good performances if their crossover operators have an ability to estimate the PDF of the population well. Thus, this study then applies our density estimation method into a simple real-coded GA to improve its search performance. Finally, through numerical experiments, we verify the effectiveness of the proposed density estimation method.