Improving Differential Evolution through Bayesian Hyperparameter Optimization
Subhodip Biswas, Debanjan Saha, Shuvodeep De, Adam D. Cobb, Swagatam Das, Brian Jalaian
Abstract
We propose a novel Evolutionary Algorithm (EA) based on the Differential Evolution algorithm for solving global numerical optimization problem in real-valued continuous parameter space. The proposed MadDE algorithm leverages the power of the multiple adaptation strategy with respect to the control parameters and search mechanisms, and is tested on the benchmark functions taken from the CEC 2021 special session & competition on single-objective bound-constrained optimization. Experimental results indicate that MadDE is able to achieve superior performance on global numerical optimization problems when compared against state-of-the-art real-parameter optimizers. We also provide a hyperparameter optimization algorithm SUBHO for improving the search performance of any EA by finding an optimal set of control parameters, and demonstrate its efficacy in enhancing MadDE's performance on the same benchmark. The source code of our implementation is publicly available at https://github.com/subhodipbiswas/MadDE.