Introducing a New Genetic Operator Based on Differential Evolution for the Effective Training of Neural Networks

Artificial neural networks are widely established models used to solve a variety of real-world problems in the fields of physics, chemistry, etc. These machine learning models contain a series of parameters that must be appropriately tuned by various optimization techniques in order to effectively address the problems that they face. Genetic algorithms have been used in many cases in the recent literature to train artificial neural networks, and various modifications have been made to enhance this procedure. In this article, the incorporation of a novel genetic operator into genetic algorithms is proposed to effectively train artificial neural networks. The new operator is based on the differential evolution technique, and it is periodically applied to randomly selected chromosomes from the genetic population. Furthermore, to determine a promising range of values for the parameters of the artificial neural network, an additional genetic algorithm is executed before the execution of the basic algorithm. The modified genetic algorithm is used to train neural networks on classification and regression datasets, and the results are reported and compared with those of other methods used to train neural networks.