Evolutionary operation setting for outcome accumulation type evolutionary rule discovery method
Abstract
Association rule analysis has been widely employed as a basic technique for data mining. Extensive research has also been conducted to apply evolutionary computing techniques to the field of data mining. This study presents a method to evaluate the settings of evolutionary operations in evolutionary rule discovery method, which is characterized by the execution of overall problem solving through the acquisition and accumulation of small results. Since the purpose of population evolution is different from that of general evolutionary computation methods that aim at discovering elite individuals, we examined the difference in the concept of settings during evolution and the evaluation of evolutionary computation by visualizing the progress and efficiency of problem solving. The rule discovery method (GNMiner) is characterized by reflecting acquired information in evolutionary operations; this study determines the relationship between the settings of evolutionary operations and the progress of each task execution stage and the achievement of the final result. This study obtains knowledge on the means of setting up evolutionary operations for efficient rule-set discovery by introducing an index to visualize the efficiency of outcome accumulation. This implies the possibility of setting up dynamic evolutionary operations in the outcome accumulation-type evolutionary computation in future studies.