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Feature selection is a crucial pre-processing step used to remove redundant information from original datasets while preserving the accuracy and processing time of classifier. The feasibility of using metaheuristic search algorithms (MSAs) such as Flow Directional Algorithm (FDA) to solve feature selection problems is one of the active research topics. Similar with other MSAs, FDA also employs conventional initialization scheme that generates initial solutions in random basis. The absence of intelligent mechanisms in conventional initialize scheme tends to generate initial populations in local optima, hence compromising the performance of algorithm to handle datasets with complex features. In this paper, a modified algorithm known as Multi Chaotic Flow Directional Algorithm (MCFDA) is proposed to solve feature selection problems with enhanced performances by leveraging the strengths of multiple chaotic maps for population initialization. A total of 12 datasets from UCI Machine Learning Repository are selected for performance evaluation of MCFDA and another four peer algorithms to solve feature selection problems. The proposed MCFFA is revealed to deliver best performances by solving 7 out of 12 datasets with the best mean classification accuracy and 6 out of 12 datasets with the least numbers of selected features.

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