Feature selection using Binary Crow Search Algorithm with time varying flight length

Crow Search Algorithm (CSA) is a simple yet effective meta-heuristic algorithm that has been applied to solve many engineering problems. In CSA, fl parameter controls the search capability of crows and AP parameter balances the trade-off between exploration and exploitation. The parameter fl is initialized to a constant value in CSA. However, CSA faces the problem of being trapped in local minima. This work proposes the solution to this problem by introducing the new concept of time varying flight length in CSA. The value of fl should be large in initial stages of algorithm in order to support random exploration and it should gradually decrease in later iterations to encourage the exploitation of good solutions found so far. The proposed approach, Binary Crow Search Algorithm with Time Varying Flight Length (BCSA-TVFL) is applied to feature selection problems in wrapper mode. Eight variants of BCSA-TVFL based on eight different transfer functions are tested. The best performing variant is then selected and compared with other state-of-the-art wrapper feature selection techniques and standard filter feature selection techniques. Performance of proposed approach is tested on 20 standard UCI datasets. Experimental result comparison shows that the proposed feature selection technique performs better than other competitors.