A genetic algorithm with entropy based initial bias for automated rule mining

The main criticism of employing genetic algorithms in data mining applications is local convergence and their long running time particularly for large datasets with large number of attributes. One solution to this problem is giving a filtering bias to initial population such that more relevant attributes get initialized with higher probability as compared to not so important attributes with respect to prediction. This paper proposes a genetic algorithm with entropy based filtering bias to initial population. Each attribute in the initial population is initialized with a probability inversely proportional to its entropy. Relevant attributes occurring more frequently in the initial population provide a good start for GA to search for better fit rules at earlier generations. The results demonstrate the efficacy and efficiency of the proposed system for automated rule mining.