Interactive genetic algorithms based on implicit knowledge model

Authors:
Yi-nan Guo;Dun-wei Gong;Ding-quan Yang
Affiliations:
School of Information and Electronic Engineering, China University of Mining, and Technology, Xuzhou, Jiangsu, China;School of Information and Electronic Engineering, China University of Mining, and Technology, Xuzhou, Jiangsu, China;School of Information and Electronic Engineering, China University of Mining, and Technology, Xuzhou, Jiangsu, China
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

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Abstract

Interactive genetic algorithms depend on more knowledge embodied in evolution than other genetic algorithms for explicit fitness functions. But there is a lack of systemic analysis about implicit knowledge of interactive genetic algorithms. Aiming at above problems, an interactive genetic algorithm based on implicit knowledge model is proposed. The knowledge model consisting of users' cognition tendency and the degree of users' preference is put forward, which describes implicit knowledge about users' cognitive and preference. Based on the concept of information entropy, a series of novel operators to realize extracting, updating and utilizing knowledge are illustrated. To analyze the performance of knowledge-based interactive genetic algorithms, two novel measures of dynamic stability and the degree of users' fatigue are presented. Taking fashion design system as a test platform, the rationality of knowledge model and the effective of knowledge induced strategy are proved. Simulation results indicate this algorithm can alleviate users' fatigue and improve the speed of convergence effectively.