Genetic algorithm restricted by tabu lists in data mining

The present work shows an implementation of a genetic algorithm (GA) integrated with tabu lists to generate a classifier tool for a data mining task. The choice of the GA paradigm is partially justified by its great capacity to deal with noise, invalid or inexact data, and its easy adaptation to different data domains. The GA algorithm uses a tabu list to restrict the selection process. This restriction allows the creation of set of potential rules for the classifier tool. This strategy was proposed by Kurahashi et al. (2000), for multimodal and multiobjective function optimization and represents an alternative to sharing methods. In this work, the behavior of this approach in data mining task was analyzed. Experiments were performed on five databases and results were compared with 34 other classifying algorithms. After that, noise was added to the databases and a new set of experiments was performed. The results show that the algorithm proposed is efficient and robust. And the strategy used to maintain the diversity was considered valid, since the algorithm was able to keep its accuracy in categorization even for smaller populations