Applying YUKI Algorithm to Interactive Evolutionary Computation

Abstract

We apply the YUKI algorithm to interactive evolutionary computation as a new approach in candidate solution retrieval. The YUKI algorithm searches for the optimal solution by balancing convergence and divergence of the current optimal solution candidates. The proposed method presents various solution candidates through the convergence and divergence of the candidate solution group when a user’s Kansei space includes complexity and multimodality. We investigate the performance evaluation of the proposed algorithm through numerical simulations using a pseudo-user model which evaluates the candidate solution instead of real users. The simulation results indicate that, compared to traditional interactive genetic algorithm and interactive tabu search algorithm, the proposed algorithm can effectively balance convergence and diffusion in the search for candidate solutions, resulting in a tendency for higher evolutionary performance when the pseudo user has several preference points.