A New Fuzzy Adaptive Multi-Population Genetic Algorithm Based Spam Filtering Method

Internet e-mails have become a common medium of communication for nearly every one. With the fast growing, spam interferes with valid email, and bothers users. This paper proposes a new fuzzy adaptive multi-population genetic algorithm (FAMGA), in order to automatically find the best feature subset to classify spam e-mails. FAMGA consists of multiple subpopulations, and each population runs independently. We design two fuzzy controllers to adjust the crossover rate and the size of each subpopulation, in order to prevent premature convergence of the population. Two publicly available benchmark corpora for spam filtering, the PU1 and Ling-Spam, are used in our experiments. The results of experiments show that the proposed method improves the performance of spam filtering, and is better than other methods of feature selection.