Firefly Programming for Symbolic Regression Problems

Abstract

Symbolic regression is the process of finding a mathematical formula that fits a specific set of data by searching in different mathematical expressions. This process requires great accuracy in order to reach the correct formula. In this paper, we will present a new method for solving symbolic regression problems based on the firefly algorithm. This method is called Firefly Programming (FP). The results of applying firefly programming algorithm to some symbolic regression benchmark problems will be compared to the results of Genetic Programming (GP) and Artificial Bee Colony Programming (ABCP) methods.