Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dawkins, R.: The Blind Watchmaker. Longman, UK (1986)
Dozier, G., Carnahan, B., Seals, C., et al.: An Interactive Distributed Evolutionary Algorithm (IDEA) for Design. In: The IEEE International Conference on Systems, Man and Cybernetics, pp. 418–422. IEEE Press, New York (2005)
Takagi, H., Ohsaki, M.: Interactive Evolutionary Computation-based Hearing Aid Fitting. J. IEEE Transactions on Evolutionary Computation 11, 414–427 (2007)
Kim, H.S., Cho, S.B.: An Efficient Genetic Algorithm with Less Fitness Evaluation by Clustering. In: IEEE Congress on Evolutionary Computation, pp. 887–894. IEEE Press, New York (2001)
Gong, D.W., Yuan, J., Ma, X.P.: Interactive Genetic Algorithms with Large Population Size. In: Proceedings of the 2008 IEEE Congress on Evolutionary Computation, CEC 2008, pp. 1678–1685. IEEE Press, New York (2008)
Ren, J., Gong, D.W.: Interactive Genetic Algorithms with Variational Population Size. In: Huang, D.-S., Jo, K.-H., Lee, H.-H., Kang, H.-J., Bevilacqua, V. (eds.) ICIC 2009. LNCS, vol. 5755, pp. 64–73. Springer, Heidelberg (2009)
Llora, X., Sastry, K., Goldberg, D.E., et al.: Combating User Fatigue in Igas: Partial Ordering, Support Vector Machines, and Synthetic Fitness, Illinois Genetic Algorithms Lab. University of Illinois, Urbana-Champaign (2005)
Ecemis, I., Bonabeau, E., Ashburn, T.: Interactive Estimation of Agent-Based Financial Markets Models: Modularity and Learning. In: Genetic and Evolutionary Computation Conference, pp. 1897–1904. ACM Press, New York (2005)
Gong, D.W., Guo, G.S.: Interactive Genetic Algorithms with Interval Fitness of Evolutionary Individuals. In: Dynamics of Continuous, Discrete and Impulsive Systems. Series B, pp. 446–450 (2007)
Gong, D., Sun, X.: Surrogate Models Based on Individual’s Interval Fitness in Interactive Genetic Algorithms. Chinese Journal of Electronics, 689–694 (2009)
Sun, X.Y., Gong, D.W., Ma, X.P.: Directed Fuzzy Graph Based Surrogate Model Assisted Interactive Genetic Algorithms with Uncertain Individual’s Fitness. In: 2009 IEEE Congress on Evolutionary Computation, CEC 2009, pp. 2395–2402. IEEE Press, New York (2009)
Zhou, Z.H., Li, M.: Semi-Supervised Regression with Co-Training Style Algorithms. IEEE Transactions on Knowledge and Data Engineering 19, 1479–1493 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Sun, X., Ren, J., Gong, D. (2010). Interval Fitness Interactive Genetic Algorithms with Variational Population Size Based on Semi-supervised Learning. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-13278-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
Online ISBN: 978-3-642-13278-0
eBook Packages: Computer ScienceComputer Science (R0)