Fitness Value Curves Prediction in the Evolutionary Process of Genetic Algorithms Applied to Benchmark Function

Abstract

This work intends to adopt fitness curves prediction from Genetic Algorithms (GAs) proposed in [Almeida et al. 2021], in the context of a more complex function, which is the Schwefel benchmark function. The prediction is performed with the knowledge only of the GA initialization parameters, using the Random Forest model. This approach addresses the main gap in the original work achieving good results, which makes this approach more promising.