Abstract
Human fatigue is a key problem existing in interactive genetic algorithms which limits population size and generations. Aiming at this problem, evaluation strategies based on surrogate models are presented, in which some individuals are evaluated by models instead of human. Most of strategies adopt fixed substitution proportion, which can not alleviate human fatigue farthest. A novel evaluation strategy with variable substitution proportion is proposed. Substitution proportion lies on models’ precision and human fatigue. Different proportion cause three evaluation phases, which are evaluated by human only, mixed evaluated by human and the model, evaluated by the model only. In third phase, population size is enlarged. Taking fashion evolutionary design system as an example, the validity of the strategy is proved. Simulation results indicate the strategy can effectively alleviate human fatigue and improve the speed of convergence.
This is a preview of subscription content, log in via an institution to check access.
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
Biles, J.A., Anderson, P.G., Loggi, L.W.: Neural Network Fitness Functions for A Musical IGA. In: Proc.of the Symposium on Intelligent Industrial Automation & Soft Computing, pp. 39–44 (1996)
Takagi, H.: Interactive Evolutionary Computation: System Optimization Based on Human Subjective Evolution. In: Proc.of IEEE Conference on Intelligent Engineering System, pp. 1–6 (1998)
Zhou, Y., Gong, D.W., Hao, G.S., et al.: Neural Network Based Phase Estimation of Individual Fitness in Interactive Genetic Algorithm. Control and Decision 20, 234–236 (2005)
Wang, S.F., Wang, S.H., Wang, X.F.: Improved Interactive Genetic Algorithm Incorporating with SVM and Its Application. Journal of Data Acquisition & Processing 18, 429–433 (2003)
Lee, J.Y., Cho, S.B.: Sparse Fitness Evaluation for Reducing User Burden in Interactive Genetic Algorithm. In: Proc. of IEEE International Fuzzy Systems, pp. 998–1003 (1999)
Sugimoto, F., Yoneyama, M.: An Evaluation of Hybrid Fitness Assignment Strategy in Interactive Genetic Algorithm. In: 5th Workshop on Intelligent & Evolutionary Systems, pp. 62–69 (2001)
Guo, Y.N., Cheng, J., Dun, W.G.: Knowledge-inducing Interactive Genetic Algorithms Based on Multi-agent. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 769–779. Springer, Heidelberg (2006)
Kim, H., Cho, S.B.: Application of Interactive Genetic Algorithm to Fashion Design. Engineering Applications of Artificial Intelligence 13, 635–644 (2000)
Hao, G.S., Gong, D.W., Shi, Y.Q.: Interactive Genetic Algorithm Based on Landscape of Satisfaction and Taboos. Journal of China University of Mining & Technology 34, 204–208 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Yn., Gong, Dw., Wang, H. (2007). Adaptive Evaluation Strategy Based on Surrogate Model. In: Jacko, J.A. (eds) Human-Computer Interaction. Interaction Design and Usability. HCI 2007. Lecture Notes in Computer Science, vol 4550. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73105-4_52
Download citation
DOI: https://doi.org/10.1007/978-3-540-73105-4_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73104-7
Online ISBN: 978-3-540-73105-4
eBook Packages: Computer ScienceComputer Science (R0)