Solving classification problems using Traceless Genetic Programming

TL;DR

This paper introduces Traceless Genetic Programming (TGP), a novel GP method that does not explicitly store evolved programs, demonstrating competitive performance on real-world classification tasks from PROBEN1.

Contribution

The paper presents TGP, a new genetic programming approach that differs by not explicitly storing programs, and shows its effectiveness on real-world classification problems.

Findings

TGP performs comparably or better than existing GP methods.

TGP is effective on PROBEN1 classification problems.

Numerical experiments validate TGP's competitive performance.

Abstract

Traceless Genetic Programming (TGP) is a new Genetic Programming (GP) that may be used for solving difficult real-world problems. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. In this paper, TGP is used for solving real-world classification problems taken from PROBEN1. Numerical experiments show that TGP performs similar and sometimes even better than other GP techniques for the considered test problems.