Abstract
Traditional interactive genetic algorithms often have small population size because of a limited human-computer interface and user fatigue, which restricts these algorithms’ performances to some degree. In order to effectively improve these algorithms’ performances and alleviate user fatigue, we propose an interactive genetic algorithm with variational population size in this paper. In the algorithm, the whole evolutionary process is divided into two phases, i.e. fluctuant phase and stable phase of the user’s cognition. In fluctuant phase, a large population is adopted and divided into several coarse clusters according to the similarity of individuals. The user only evaluates these clusters’ centers, and the other individuals’ fitness is estimated based on the acquired information. In stable phase, the similarity threshold changes along with the evolution, leading to refined clustering of the population. In addition, elitist individuals are reserved to extract building blocks. The offspring is generated based on these building blocks, leading to a reduced population. The proposed algorithm is applied to a fashion evolutionary design system, and the results validate its efficiency.
This work was supported by NSFC with grant No.60775044 and Program for New Century Excellent Talents in University with grant No.NCET-07-0802.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Dawkins, R.: The Blind Watchmaker. Longman, Harlow (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)
Kagawa, T., Nishino, H., Utsumiya, K.: A Color Design Assistant Based on User’s Sensitivity. In: The IEEE International Conference on Systems, Man and Cybernetics, pp. 974–979. IEEE Press, Los Alamitos (2003)
Takagi, H., Ohsaki, M.: Interactive Evolutionary Computation-based Hearing Aid Fitting. J. IEEE Transactions on Evolutionary Computation 11, 414–427 (2007)
Lee, J.Y., Cho, S.B.: Sparse Fitness Evaluation for Reducing User Burden in Interactive Algorithm. In: 1999 IEEE International Fuzzy Systems Conference, pp. 998–1003. IEEE Press, New York (1999)
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 (2001)
Gong, D.W., Yuan, J., Ma, X.P.: Interactive Genetic Algorithms with Large Population Size. In: 2008 IEEE Congress on Evolutionary Computation, pp. 1678–1685. IEEE Press, New York (2008)
Gong, D.W., Hao, G.S., Zhou, Y., Guo, Y.N.: Theory and Applications of Interactive Genetic Algorithms. Defense Industry Press, Beijing (2007) (in Chinese)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ren, J., Gong, Dw., Sun, Xy., Yuan, J., Li, M. (2009). Interactive Genetic Algorithms with Variational Population Size. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-04020-7_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04019-1
Online ISBN: 978-3-642-04020-7
eBook Packages: Computer ScienceComputer Science (R0)