Difference-based firefly programming for symbolic regression problems

Abstract

Automatic programming is a type of programming that has the ability to analyze and solve problems using the principles of symbolic regression analysis. These methods can solve complex problems regardless of whether they have a specific pattern or not. In this work, we are going to introduce the difference-based firefly programming (DFP) method as an improved version of the standard firefly programming method. We have analyzed the performance of this new improved method, which will be described in detail within the scope of this work. In order to evaluate the performance of the newly presented method, the results have been compared to the results of the standard method and the results of other methods that are used to solve the same type of problems. DFP has been used also in forecasting and modeling a real-world time-series problem, where it showed good performance too. In general, the results demonstrated the improved performance of the newly introduced method and showed its ability to efficiently solve complex problems.