Nam Deok Cho , Ki Tae Kim

The KIPS Transactions:PartB , Vol. 12, No. 3, pp. 349-356, Jun. 2005
10.3745/KIPSTB.2005.12.3.349,   PDF Download:

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 AI 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. Strategics aimed at solving these problems in a machine-learning fashion includes Genetic Programming and Evolving Neural Networks. But thelearning 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 quality 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.

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