Abstract
Grey Wolf Optimiser (GWO) is the recently proposed meta-heuristic algorithm inspired by grey wolves. The original version of GWO has been proposed to solve problems in continuous search spaces. However, there are many optimisation problems in discrete binary search spaces such as feature selection and dimensionality reduction. We applied eight transfer functions in our BGWO and compared their performance. The test functions whose minimum points are gotten at 0 are selected as bench functions but also those functions their minimum value are gotten at 1.
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