Discussion of a Crossover Method using a Probabilistic Model for interactive Genetic Algorithm

Abstract

In this research, we applied an interactive Genetic Algorithm (iGA) to a product recommendation system. Products that suit a user's preference can be presented by applying iGA to the system and learning the user's preference. However, if the user's preference is biased, the dependency among design variables should be considered. Therefore, we proposed an offspring generation mechanism taking this dependency into consideration. In the proposed method, we first apply a clustering technique to the archived individuals selected by a user, and then construct a Probabilistic Model for crossover based on the clustering results. We discussed the effectiveness of the proposed mechanisms by experimenting with iGA for selecting colors and figures of symbols.