Shuffled Frog-Leaping Programming for Solving Regression Problems

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fail, the evolutionary computations are widely studied and applied to solve real-world problems. One of the famous algorithms in an optimization problem is the shuffled frog leaping algorithm (SFLA), which is inspired by the behavior of frogs to find the highest quantity of the available food by searching their environment both locally and globally. The results of SFLA prove that it is competitively effective to solve problems. In this paper, Shuffled Frog Leaping Programming (SFLP) inspired by SFLA is proposed as a novel type of automatic programming model to solve the symbolic regression problems based on tree representation. Also, in SFLP, a new mechanism is proposed for improving constant numbers in the tree structure. In this way, different domains of mathematical problems can be addressed with the use of the proposed method. To find out about the performance of the generated solutions by SFLP, various experiments are conducted using several benchmark functions. The results obtained are also compared with other evolutionary programming algorithms like BBP, GSP, GP, and many variants of GP.