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http://dx.doi.org/10.3745/KIPSTB.2005.12B.3.349

A Coevolution of Artificial-Organism Using Classification Rule And Enhanced Backpropagation Neural Network  

Cho Nam-Deok (소프트캠프(주))
Kim Ki-Tae (중앙대학교 공과대학컴퓨터공학부)
Publication Information
The KIPS Transactions:PartB / v.12B, no.3, 2005 , pp. 349-356 More about this Journal
Abstract
Artificial Organism-used application areas are expanding at a break-neck speed with a view to getting things done in a dynamic and Informal environment. A use of general programming or traditional hi methods as the representation of Artificial Organism behavior knowledge in these areas can cause problems related to frequent modifications and bad response in an unpredictable situation. Strategies aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But the learning method of Artificial-Organism is not good yet, and can't represent life in the environment. With this in mind, this research is designed to come up with a new behavior evolution model. The model represents behavior knowledge with Classification Rules and Enhanced Backpropation Neural Networks and discriminate the denomination. To evaluate the model, the researcher applied it to problems with the competition of Artificial-Organism in the Simulator and compared with other system. The survey shows that the model prevails in terms of the speed and Qualify of learning. The model is characterized by the simultaneous learning of classification rules and neural networks represented on chromosomes with the help of Genetic Algorithm and the consolidation of learning ability caused by the hybrid processing of the classification rules and Enhanced Backpropagation Neural Network.
Keywords
Enhanced Backpropagation Neural Network; Coevolution; Classification Rule; Simulation;
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1 Lee, K.-J., Zhang, B.-T., 'Learning Robot Behaviors by Evolving Genetic Programs', Proc. of the 26th International Conference on Industrial Electronics, Control and Instrumentation (IECON-2000), Vol.4, pp.2867-2872, 2000   DOI
2 Kubica, J. and Rieffel, E., 'Collaborating with a Genetic Programming System to Generate Modular Robotic Code', A joint meeting of the eleventh International Conference on Genetic Algorithms (ICGA-2002) and the seventh Annual Genetic Programming Conference (GP-2002)
3 Koza, J.R., 'Genetic Programming: On the programming of computers by means of natural selection', MIT Press. ISBN 0-262-11170-5, 1992
4 Malrey Lee, 'Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm', Journal of Information Science, 155(1-2), pp.43-60, 2003   DOI   ScienceOn
5 Collins, R.J., 'Studies in Artificial Evolution', Phd Thesis, Philosophy in Computer Science, University of California, Los Angeles, 1992
6 Holland, J.H., 'Adaptation in Natural and Artifical Systems', University of Michigan Press. Reprinted by MIT Press, 1975
7 Booker, L.B., 'Classfier systems, endogenous fitness, and delayed rewards: A preliminary investigation', Proc. of the International Workshop on Learning Classifier Systems (IWLCS-2000), 2000
8 Goldberg, D.E., 'Genetic Algorithms in Search, Optimization and Machine Learning', Addison-Wesley. ISBN0-201-15767-5, 1989
9 Langton, C., 'Artificial LifeII.', Addison Wesley. pp.1-47, 1989
10 Holland, J.H., Reitman, J.S., 'Cognitive Systems Based on Adaptive Algorithms', Pattern-Directed Inference Systems, Academic Press, NY, 1978
11 Smith, S.F., 'A Learning System Based on Genetic Adaptive Algorithms', Ph.D. Thesis, University Pittsburgh, 1980
12 조경달, '분류 규칙과 신경망을 이용한 가상 로봇의 행동 진화', 박사학위 논문, 중앙대학교 컴퓨터공학과, 2004
13 김기태, '지능형 컴퓨터의 처리를 위한 인공지능의 기법과 응용', 도서출판 기한재, pp.269-299, 1998
14 Lee, K.-J., Zhang, B.-T., 'Learning Robot Behaviors by Evolving Genetic Programs', Proc. of the 26th International Conference on Industrial Electronics, Control and Instrumentation (IECON-2000), Vol.4, pp.2867-2872, 2000   DOI
15 배환국, '인공 유기체 집단간의 경쟁을 통한 상호진화에 관한 연구', 석사학위 논문, 중앙대학교 컴퓨터공학과, 1996