Abstract
Binary versions of evolutionary algorithms have emerged as alternatives to the state of the art methods for optimization in binary search spaces due to their simplicity and inexpensive computational cost. The adaption of such a binary version from an evolutionary algorithm is based on a transfer function that maps a continuous search space to a discrete search space. In an effort to identify the most efficient combination of transfer functions and algorithms, we investigate binary versions of Gravitational Search, Bat Algorithm, and Dragonfly Algorithm along with two families of transfer functions in unimodal and multimodal single objective optimization problems. The results indicate that the incorporation of the v-shaped family of transfer functions in the Binary Bat Algorithm significantly outperforms previous methods in this domain.
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