Toward global optimization of case-based reasoning systems for financial forecasting

This paper presents a simultaneous optimization method of a case-based reasoning (CBR) system using a genetic algorithm (GA) for financial forecasting. Prior research proposed many hybrid models of CBR and the GA for selecting a relevant feature subset or optimizing feature weights. Most research used the GA for improving only a part of architectural factors of the CBR model. However, the performance of the CBR model may be enhanced when these factors are simultaneously considered. In this study, the GA simultaneously optimizes multiple factors of the CBR system. Experimental results show that a GA approach to simultaneous optimization of the CBR model outperforms other conventional approaches for financial forecasting.