Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
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.
Keywords
Cite
@article{arxiv.2110.13608,
title = {Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems},
author = {Mihai Oltean and Crina Grosan},
journal= {arXiv preprint arXiv:2110.13608},
year = {2021}
}
Comments
9 figures. arXiv admin note: text overlap with arXiv:2110.02014