SDGP: A developmental approach for traveling salesman problems

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

@InProceedings{Ouyang:2013:CIPLS,

• author = "Jin Ouyang and Thomas Weise and Alexandre Devert and Raymond Chiong",
• title = "SDGP: A developmental approach for traveling salesman problems",
• booktitle = "IEEE Workshop on Computational Intelligence In Production And Logistics Systems (CIPLS 2013)",
• year = "2013",
• month = apr,
• pages = "78--85",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/CIPLS.2013.6595203",
• abstract = "This paper presents an Evolutionary Algorithm using a new ontogenic approach, called Staged Developmental Genetic Programming (SDGP), for solving symmetric Travelling Salesman Problems (TSPs). In SDGP, a genotype-phenotype mapping (gpm) is used to refine candidate solutions to a TSP - these candidate solutions are represented as permutations. The gpm performs several development steps, in each of which such a permutation x is incrementally modified. In each iteration within a development step, the process can choose to either apply one of seven different modifications to a specific section of x or do nothing. The choice is made by the genotypes g, which are functions assigning real-valued ratings to the possible modifications. Smaller ratings are better and the best-rated modification is then applied, if its rating is lower than a given threshold. The genotypes are evolved using tree-based Genetic Programming. Comprehensive numerical simulation experiments show that our proposed algorithm scales well with the problem size and delivers competitive results compared to other state-of-the-art approaches in the TSP literature.",
• notes = "Also known as \cite{6595203}",

}

Genetic Programming entries for Jin Ouyang Thomas Weise Alexandre Devert Raymond Chiong

Citations