Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems

• URL: http://arxiv.org/abs/2110.13608v1
• Date: Thu, 7 Oct 2021 05:55:55 GMT
• Title: Using Traceless Genetic Programming for Solving Multiobjective Optimization Problems
• Authors: Mihai Oltean, Crina Grosan
• Abstract summary: 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. Two genetic operators are used in conjunction with TGP: crossover and insertion. Numerical experiments show that TGP is able to solve very fast and very well the considered test problems.
• Score: 1.9493449206135294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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