A Genetic Algorithm with Entropy Based Probabilistic Initialization and Memory for Automated Rule Mining

In recent years, Genetic Algorithms (GAs) have shown promising results in the domain of data mining. However, unreasonably long running times due to the high computational cost associated with fitness evaluations dissuades the use of GAs for knowledge discovery. In this paper we propose an enhanced genetic algorithm for automated rule mining. The proposed approach supplements the GA with an entropy based probabilistic initialization such that the initial population has more relevant and informative attributes. Further, the GA is augmented with a memory to store fitness scores. The suggested additions have a twofold advantage. Firstly, it lessens the candidate rules’ search space making the search more effective to evolve better fit rules in lesser number of generations. Secondly, it reduces number of total fitness evaluations required giving rise to a gain in running time. The enhanced GA has been employed to datasets from UCI machine learning repository and has shown encouraging results.

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