Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection

The Black Hole algorithm converges efficiently but can get trapped in local optima; Genetic Algorithms diversify the search well through crossover and mutation but converge slowly. This paper couples both into a single hybrid: a switching probability parameter controls when the algorithm follows Black Hole update rules versus Genetic Algorithm operators, combining the strengths of both. Tuning the switching probability is shown to be key — the optimally configured hybrid yields considerably better feature subsets than either algorithm run independently.

Recommended citation: Bhosale, H., Ovhal, P., Sane, A., Valadi, J.K. (2022). Improving Black Hole Algorithm Performance by Coupling with Genetic Algorithm for Feature Selection. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 114. Springer, Singapore. https://doi.org/10.1007/978-981-16-9416-5_26 https://link.springer.com/chapter/10.1007/978-981-16-9416-5_26