Classification and regression-based surrogate model-assisted interactive genetic algorithm with individual's fuzzy fitness

Interactive genetic algorithms with individual's fuzzy fitness well portray the fuzzy uncertainties of a user's cognition. In this paper, we propose an efficient surrogate model-assisted one to alleviate user fatigue by building a classifier and a regressor to approximate the user's cognition. Two reliable training data sets are obtained based on the user's evaluation credibility. Then a support vector classification machine and a support vector regression machine are trained as the surrogate models with these samples. Specifically, the input trained samples are the individuals evaluated by the user, and the output training samples of the classifier and the regressor are widths and centers of these individuals' fuzzy fitness assigned by the user, respectively. These two surrogate models are simultaneously applied to the subsequent evolutions with enlarged population size so as to alleviate user fatigue and enhance the search ability of the algorithm. We constantly update the training data sets and the surrogate models in order to guarantee the approximation precision. Furthermore, we quantitatively analyze the algorithm's performance in alleviating user fatigue and increasing more opportunities to find the optimal solutions. We also apply it to a fashion evolutionary design system to show its efficiency.