A Data-Centric Approach to Parameter Tuning, an Application to Differential Evolution

Algorithms such as Differential Evolution are currently state of the art in many fields, however, the performance of Differential Evolution is strongly influenced by the chosen values of its parameters. The most relevant parameters in Differential Evolution are the size of the population, the crossover probability, and the mutation factor. In this research, we present a novel way of tuning these parameters using neural networks. We collect data characterizing the optimization process and associate it with the result of modifying each parameter independently. We use this information to train several classification models on how to adjust each parameter. The trained models are then used to adjust the parameters after consecutive executions of Differential Evolution. Computational results using the CEC'13 benchmark suite, show that this approach is very effective and leads to a significant improvement in performance.