Case-Base Maintenance (CBM) has two important goals. On the one hand, it aims to reduce the size of the case-base. On the other hand, it has to improve the accuracy of the CBR system. CBM can be represented as a multi-objective optimization problem to achieve both goals. Multi-Objective Evolutionary Algorithms (MOEAs) have been recognised as appropriate techniques for multi-objective optimisation be- cause they perform a search for multiple solutions in parallel. In the present paper we introduce a fitness function based on the Complexity Profiling model to perform CBM with MOEA, and we compare its results against other known CBM approaches. From the experimental results, CBM with MOEA shows regularly good results in many case-bases, de- spite the amount of redundant and noisy cases, and with a significant potential for improvement.
['Eduardo Lupiani', 'Susan Craw', 'Stewart Massie', 'Jose M. Juarez', 'José T. Palma']
A Multi-Objective Evolutionary Algorithm Fitness Function for Case-Base Maintenance