Using traceless genetic programming for solving multi-objective optimization problems

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{oltean:2007:JETAI,

• author = "Mihai Oltean and Crina Grosan",
• title = "Using traceless genetic programming for solving multi-objective optimization problems",
• journal = "Journal of Experimental \& Theoretical Artificial Intelligence",
• year = "2007",
• volume = "19",
• number = "3",
• pages = "227--248",
• email = "moltean@cs.ubbcluj.ro",
• keywords = "genetic algorithms, genetic programming, multiobjective optimisation",
• ISSN = "0952-813X",
• URL = "http://www.cs.ubbcluj.ro/~moltean/tgp_moea.pdf",
• DOI = "doi:10.1080/09528130601138273",
• size = "21 pages",
• abstract = "Traceless Genetic Programming (TGP) is a Genetic Programming (GP) variant that is used in the 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 multiobjective optimisation 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.",

}

Genetic Programming entries for Mihai Oltean Crina Grosan

Citations