Interactive estimation of agent-based financial markets models: modularity and learning
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Semisupervised Regression with Cotraining-Style Algorithms
IEEE Transactions on Knowledge and Data Engineering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Interactive genetic algorithms with variational population size
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Interactive Evolutionary Computation-Based Hearing Aid Fitting
IEEE Transactions on Evolutionary Computation
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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.