This paper presents a new hybrid of Bat Algorithm (BA) based on Mutual Information (MI) and Naive Bayes called BAMI. In BAMI, MI was used to identify promising features which could potentially accelerate the process of finding the best known solution. The promising features were then used to replace several of the randomly selected features during the search initialization. BAMI was tested over twelve datasets and compared against the standard Bat Algorithm guided by Naive Bayes (BANV). The results showed that BAMI outperformed BANV in all datasets in terms of computational time. The statistical test indicated that BAMI has significantly lower computational time than BANV in six out of twelve datasets, while maintaining the effectiveness. The results also showed that BAMI performance was not affected by the number of features or samples in the dataset. Finally, BAMI was able to find the best known solutions with limited number of iterations.