Interactive evolutionary computation with evaluation characteristics of multi-IEC users

We (1) propose a method for accelerating the convergence of interactive evolutionary computation (IEC) by incorporating multiple evaluation models of previous IEC users, (2) evaluate the method's performance according to the similarity metric of users' evaluation characteristics, and (3) investigate its practical usefulness by measuring users' evaluation characteristics for real-world applications on the metric. Although conventional IEC with a function learning the current IEC user's evaluation characteristics cannot use the evaluation characteristics until the model is learned, the proposed IEC uses models learned from previous users until the current user's behavior is learned. The model from a previous IEC user whose evaluation values are most similar to those of the current IEC user is selected and used instead of the current IEC user's model till the current user's model is leaned. The viability of this method is evaluated on similarity distance of evaluation characteristics with simulation, and the simulation results are compared with the real IEC user's evaluation characteristics for four different types of real applications. Through this evaluation, we obtain a rating method for predicting the effectiveness of the proposed acceleration method for different types of IEC applications.