Developing Postfix-GP Framework for Symbolic Regression Problems

TL;DR

This paper introduces Postfix-GP, an object-oriented, GUI-enabled genetic programming framework designed for symbolic regression, demonstrating its effectiveness on benchmark problems and comparing its features with other systems.

Contribution

The paper presents a novel Postfix-GP framework with a user-friendly interface and detailed architecture for solving symbolic regression problems.

Findings

Successfully applied to benchmark symbolic regression problems

Provides visualization and analysis tools for GP runs

Features compared favorably with other GP systems

Abstract

This paper describes Postfix-GP system, postfix notation based Genetic Programming (GP), for solving symbolic regression problems. It presents an object-oriented architecture of Postfix-GP framework. It assists the user in understanding of the implementation details of various components of Postfix-GP. Postfix-GP provides graphical user interface which allows user to configure the experiment, to visualize evolved solutions, to analyze GP run, and to perform out-of-sample predictions. The use of Postfix-GP is demonstrated by solving the benchmark symbolic regression problem. Finally, features of Postfix-GP framework are compared with that of other GP systems.