A developmental solution to (dynamic) capacitated arc routing problems using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{Weise:2012:GECCO,

• author = "Thomas Weise and Alexandre Devert and Ke Tang",
• title = "A developmental solution to (dynamic) capacitated arc routing problems using genetic programming",
• booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
• year = "2012",
• editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
• isbn13 = "978-1-4503-1177-9",
• pages = "831--838",
• keywords = "genetic algorithms, genetic programming",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Philadelphia, Pennsylvania, USA",
• DOI = "doi:10.1145/2330163.2330278",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "A developmental, ontogenic approach to Capacitated Arc Routing Problems (CARPs) is introduced. The genotypes of this method are constructive heuristics specified as trees of mathematical functions which are evolved with Genetic Programming (GP). In a genotype-phenotype mapping, they guide a virtual vehicle which starts at the depot. The genotype is used to compute a heuristic value for each edge with unsatisfied demands. Local information such as the visiting costs from the current position, the remaining load of the vehicle, and the edge demands are available to the heuristic. The virtual vehicle then serves the edge with the lowest heuristic value and is located at its end. This process is repeated until all requirements have been satisfied. The resulting phenotypes are sets of tours which, in turn, are sequences of edges. We show that our method has three advantages: 1) The genotypes can be reused to seed the population in new GP runs. 2) The size of the genotypes is independent from the problem scale. 3) The evolved heuristics even work well in modified or dynamic scenarios and are robust in the presence of noise.",
• notes = "Also known as \cite{2330278} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",

}

Genetic Programming entries for Thomas Weise Alexandre Devert Ke Tang

Citations