Rule Acquisition in Data Mining Using a Self Adaptive Genetic Algorithm

Abstract

Rule acquisition is a technique of data mining that is used to deduce inferences from large databases. These inferences cannot be noticed easily without data mining. Genetic algorithms (GAs) are considered as a global search approach for optimization problems. Through the proper evaluation strategy, the best “chromosome” can be found from the numerous genetic combinations. In the self-adaptive genetic algorithm, its main thought is to let control parameter (crossover rate, mutation rate) adjusted adaptively within the proper range, thus achieve a more optimum solution. It is proved that the self-adaptive genetic algorithm is with excellent convergence and higher precision than the traditional genetic algorithm.