Evolving Case-Based Reasoning with Genetic Algorithm in Wholesaler’s Returning Book Forecasting

• Published in: Advances in Natural Computation pp. 205 - 214
• Main Authors: Chang, Pei-Chann, Wang, Yen-Wen, Ting, Ching-Jung, Lai, Chien-Yuan, Liu, Chen-Hao
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Sales; Case based reasoning; Genetic algorithm; Artificial intelligence; Forecasting; Hybrid system
• ISBN: 9783540283201; 354028320X; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539902_24

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.