Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems

TL;DR

This paper introduces Traceless Genetic Programming (TGP), a novel GP variant that optimizes multi-objective problems efficiently by focusing on output rather than program structure, demonstrating superior speed and performance.

Contribution

The paper applies TGP to multi-objective optimization, showcasing its effectiveness and speed, which is a novel application for this GP variant.

Findings

TGP solves multi-objective problems quickly and effectively.

TGP outperforms traditional GP in the tested scenarios.

TGP does not explicitly store evolved programs, reducing complexity.

Abstract

Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in cases where the focus is rather the output of the program than the program itself. The main difference between TGP and other GP techniques is that TGP does not explicitly store the evolved computer programs. Two genetic operators are used in conjunction with TGP: crossover and insertion. In this paper, we shall focus on how to apply TGP for solving multi-objective optimization problems which are quite unusual for GP. Each TGP individual stores the output of a computer program (tree) representing a point in the search space. Numerical experiments show that TGP is able to solve very fast and very well the considered test problems.