Maintaining case-based reasoning systems using a genetic algorithms approach

摘要

The success of a case-based reasoning (CBR) system largely depends on an effective maintenance of its case-base. This study proposes a genetic algorithms (GAs) approach to the maintenance of CBR systems. This approach automatically determines the representation of cases and indexes relevant attributes to grasp the rapidly changing environment around the system. In this study, the proposed model is applied to stock market analysis. Experimental results show that the proposed model outperforms conventional CBR systems.