Interval Fitness Interactive Genetic Algorithms with Variational Population Size Based on Semi-supervised Learning

In order to alleviate user fatigue and improve the performance of interactive genetic algorithms(IGAs) in searching, we introduce a co-training semi-supervised learning(CSSL)algorithm into interval fitness IGAs with large and variational population size The CSSL is adopted to model the user's preference so as to estimate abundant of unevaluated individuals' fitness First, the method to select the labeled and unlabeled samples for CSSL is proposed according to the clustering results of the large size population Combined with the approximation precision of two co-training learners, an efficient strategy for selecting high reliable unlabeled samples to label is given Then, we adopt the CSSL mechanism to train two RBF neural networks for establishing the surrogate model with high precision and generalization In the evolution, the surrogate model estimates individuals' fitness and it is managed to guarantee the approximation precision based on its estimation error The proposed algorithm is applied to a fashion evolutionary design system, and the experimental results show its efficiency.

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