Self-Adapting Approach in Parameter Tuning for Differential Evolution

Higher expectation has been requested from artificial intelligence (AI) owing to its success in various applications and domains. The use of AI is no longer limited to solve static optimization problems, but to perform well in dynamic optimization problems as well. The performance of AI in problem solving depends greatly on its own control parameters. The set of parameters which has been tuned to solve current optimization problem may not lead to the same performance if there is a shift or change in the optimization problem. To ensure its functionality in such condition, a machine learning needs to be able to self-determine its own control parameters. In short, a machine learning needs to be adaptive. Evolutionary algorithms (EAs) associated with adaptive ability turn out to be a potential solution under this condition. Therefore, our research focuses on the use of self-adaptive approach in parameter tuning in EAs, specifically in differential evolution (DE). Given that our proposed DE is no longer depending on a user to determine its control parameters, we are interested to know whether the self-adapting parameters will ensure good performance from DE or not. Two versions of DEs with the ability to self-adapt their parameters are developed. Most of DE related studies have suggested certain ranges of parameters to ensure appropriate operation of standard DE. In this research, we take the opportunity to confirm whether the ranges of self-adapting parameters fall within the suggested ranges or not. The experimental results have shown that both self-adapting DEs perform adequately well in 20 different benchmark problems without depending on user to determine the parameters explicitly. Besides that, it is interesting to find out the control parameters of the self-adapting DEs are not necessarily within the suggested ranges and they are still performing adequately well.