Abstract
Interactive genetic algorithms lack a common model to effectively integrate different assistant evolution strategies including knowledge-based methods and fitness assignment strategies.Aiming at the problems,knowledge-based interactive genetic algorithm based on multi-agent is put forward in the paper combined with the flexibility of multi-agent systems.Five kinds of agents are abstracted based on decomposed-integral strategy of MAS.A novel implicit knowledge model and corresponding inducing strategy are proposed and realized by knowledge-inducing agent.A novel substitution strategy for evaluating fitness by an online model instead of human is proposed and implemented in fitness-estimation agent.State-switch conditions of above agents are given using agent-oriented programming. Taking fashion design system as a testing platform, the rationality of the model and the effective of assistant evolution strategies proposed in the paper are validated. Simulation results indicate this algorithm can effectively alleviate users’ fatigue and improve the speed of convergence.
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
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)
Giraldez, R., Aguilar-ruiz, J.S., Riquelme, J.C.: Knowledge-based Fast Evaluation for Evolutionary Learning. IEEE Trans. on SMC-Part C: Application and Review 35, 254–261 (2005)
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)
Yong, Z., Dun-wei, G., Guo-sheng, H., 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)
Joo-Young, L., Sung-Bae, C.: 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)
Furuya, H.: Genetic Algorithm and Multilayer Neural Network. In: Proc.of Calculation and Control, pp. 497–500 (1998)
Hui, G., Yu-en, G., Zhen-xi, Z.: A Knowledge Model Based Genetic Algorithm. Computer engineering 26, 19–21 (2000)
Sebag, M., Ravise, C., Schoenauer, M.: Controlling Evolution by Means of Machine Learning. Evolutionary Programming, 57–66 (1996)
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)
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)
Reynolds, G.R.: An Introduction to Cultural Algorithms. In: Proc. of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139 (1994)
Parmee, I.C., Cvetkovic, D., Watson, A.H.: Multi-objective Satisfaction within An Interactive Evolutionary Design Environment. Evolutionary Computation, 197–222 (2000)
Shan-shan, J., xian-bing, C., xi-fa, W.: User’s Agent Model and Design Using IGA. Pattern recognition and Artificial Intelligence 17, 244–249 (2004)
Zhi-hua, L., De-zhao, C., Shang-xu, H.: The Construction and Realization of Intelligent Multi-agent System to Implement Genetic Algorithm. Computer Engineering and Application 11, 41–43 (2002)
Weicai, Z., Jing, L., Mingzhi, X., Licheng, J.: A Multiagent Genetic Algorithm for Global Numerical Optimization. IEEE Trans. on System, Man, and Cybernetics-Part B 34, 1128–1141 (2004)
Liu, J., Zhong, W., Jiao, L.: A Multiagent Evolutionary Algorithm for Constraint Satisfaction Problems. IEEE Trans. on System, Man, and Cybernetics-Part B 36, 54–73 (2006)
Yi’nan, G., Dunwei, G., Yong, Z.: Cooperative Interactive Evolutionary Computation Model Based on Multi-agent System. Journal of System Simulation 17, 1548–1552 (2005)
van Breemen, A.J.N.: Agent-Based Multi-Controller Systems, Ph.D. thesis. Twente University, Netherlands (2001)
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
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, Yn., Cheng, J., Gong, Dw., Yang, Dq. (2006). Knowledge-Inducing Interactive Genetic Algorithms Based on Multi-agent. In: Jiao, L., Wang, L., Gao, Xb., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881070_101
Download citation
DOI: https://doi.org/10.1007/11881070_101
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45901-9
Online ISBN: 978-3-540-45902-6
eBook Packages: Computer ScienceComputer Science (R0)