Evolving case-based reasoning with genetic algorithm in wholesaler's returning book forecasting

Authors:
Pei-Chann Chang;Yen-Wen Wang;Ching-Jung Ting;Chien-Yuan Lai;Chen-Hao Liu
Affiliations:
Department of Industrial Engineering and Management, Yuan-Ze University, Nei-Li, Tao Yuan, Taiwan, R.O.C;Department of Industrial Engineering and Management, Yuan-Ze University, Nei-Li, Tao Yuan, Taiwan, R.O.C;Department of Industrial Engineering and Management, Yuan-Ze University, Nei-Li, Tao Yuan, Taiwan, R.O.C;Department of Industrial Engineering and Management, Yuan-Ze University, Nei-Li, Tao Yuan, Taiwan, R.O.C;Department of Industrial Engineering and Management, Yuan-Ze University, Nei-Li, Tao Yuan, Taiwan, R.O.C
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Year:
2005

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Abstract

In this paper, a hybrid system is developed by evolving Case-Based Reasoning (CBR) with Genetic Algorithm (GA) for reverse sales forecasting of returning books. CBR systems have been successfully applied in several domains of artificial intelligence. However, in conventional CBR method each factor has the same weight which means each one has the same influence on the output data that does not reflect the practical situation. In order to enhance the efficiency and capability of forecasting in CBR systems, we applied the GAs method to adjust the weights of factors in CBR systems, GA/CBR for short. The case base of this research is acquired from a book wholesaler in Taiwan, and it is applied by GA/CBR to forecast returning books. The result of the prediction of GA/CBR was compared with other traditional methods.