Interactive Genetic Algorithms Based on Implicit Knowledge Model
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.
KeywordsGenetic Algorithm Dynamic Stability Information Entropy Knowledge Model Implicit Knowledge
Unable to display preview. Download preview PDF.
- 1.Takagi, H.: Interactive Evolutionary Computation:Fusions of The Capabilities of EC Optimization and Human Evaluation. In: Proc. of the IEEE CEC, pp. 1275–1296 (2001)Google Scholar
- 2.Giraldez, R., Aguilar-ruiz, J.S., Riquelme, J.C.: Knowledge-based Fast Evaluation for Evolutionary Learning. IEEE trasaction on SMC-Part C: Application and Review 35, 254–261 (2005)Google Scholar
- 3.Furuya, H.: Genetic Algorithm and Multilayer Neural Network. In: Proc. of Calculation and Control, pp. 497–500 (1998)Google Scholar
- 4.Hui, G., Yu-en, G., Zhen-xi, Z.: A Knowledge Model Based Genetic Algorithm. Computer engineering 26, 19–21 (2000)Google Scholar
- 5.Sebag, M., Ravise, C., Schoenauer, M.: Controlling Evolution by Means of Machine Learning. Evolutionary Programming, pp. 57–66 (1996)Google Scholar
- 6.Lei, F., huai-zhong, R., Yu, J., et al.: Conduct Evolution Using Induction Learning. Journal of University of Science and Technology of China 31, 565–634 (2001)Google Scholar
- 7.Handa, H., Horiuchi, T., Katai, O., et al.: Co-evolutionary GA with Schema Extraction by Machine Learning Techniques and Its Application to Knapsack Problem. In: IEEE Conference of Evolutionary Computation, pp. 1213–1219 (2001)Google Scholar
- 8.Reynolds, G.R.: An Introduction to Cultural Algorithms. In: Proc. of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139 (1994)Google Scholar
- 9.Guo-sheng, H., Dun-wei, G., You-qun, S.: Interactive Genetic Algorithm Based on Landscape of Satisfaction and Taboos. Journal of China University of Mining & Technology, 204–208 (2005)Google Scholar
- 10.Wen-xiu, Z., Yi, L.: Mathematical Foundation of Genetic Algorithms. Jiaotong University Press, Xi’an (1999)Google Scholar
- 11.Driels, M.: Linear Control Systems Engineering. McGraw-Hill, New York (1996)Google Scholar