Applying Knowledge of Users with Similar Preference to Construct Surrogate Models of IGA

Abstract

Interactive genetic algorithms (IGAs) are effective methods of solving optimization problems with qualitative indices. The problem of user fatigue resulting from the user’s evaluations has a negative influence on the performance of these algorithms. Employing various surrogate models to evaluate (a part of ) individuals instead of a user is a feasible approach to solve the problem. Previous studies have not fully utilized knowledge provided by users with a similar preference when constructing these models. The problem of constructing surrogate models by using the knowledge of users with a similar preference was focused in this study. Users with a similar preference participating in the evolution were identified by using the collaborative filtering algorithm based on the nearest neighbor, and the individuals evaluated by these users were chosen as a part of samples for training the surrogate model of the current user’s cognition. The proposed method was applied to an evolutionary fashion design system, and the experimental results showed that the proposed method can improve the capability in exploration on the premise of greatly alleviating user fatigue.