Surrogate models for user's evaluations base on weighted support vector machine in IGAs
Interactive genetic algorithms (IGAs) are effective methods of tackling optimization problems involving qualitative indices by incorporating a user's evaluations into traditional genetic algorithms. The problem of user fatigue resulting from the user's evaluations, however, has a negative influence on the performance of these algorithms. Substituting the user's evaluations with various surrogate models is beneficial to alleviate user fatigue. Previous studies, however, have not taken full advantage of information provided by samples obtained earlier when constructing or updating these models. We focus on the issue of user fatigue in this study, and present a novel method of effectively alleviating user fatigue by substituting the user's evaluations with a weighted support vector machine (WSVM) and by incorporating it with the mechanism of transfer learning. The proposed method is applied to the fashion evolutionary design system and compared with previous effective IGAs. The experimental results confirm the advantage of the proposed method in both alleviating user fatigue and improving the precision of the surrogate model.