Generation of VNS Components with Grammatical Evolution for Vehicle Routing

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "John H. Drake and Nikolaos Kililis and Ender Ozcan",
  title =        "Generation of VNS Components with Grammatical
                 Evolution for Vehicle Routing",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "25--36",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_3",
  abstract =     "The vehicle routing problem (VRP) is a family of
                 problems whereby a fleet of vehicles must service the
                 commodity demands of a set of geographically scattered
                 customers from one or more depots, subject to a number
                 of constraints. Early hyper-heuristic research focused
                 on selecting and applying a low-level heuristic at a
                 given stage of an optimisation process. Recent trends
                 have led to a number of approaches being developed to
                 automatically generate heuristics for a number of
                 combinatorial optimisation problems. Previous work on
                 the VRP has shown that the application of
                 hyper-heuristic approaches can yield successful
                 results. In this paper we investigate the potential of
                 grammatical evolution as a method to evolve the
                 components of a variable neighbourhood search (VNS)
                 framework. In particular two components are generated;
                 constructive heuristics to create initial solutions and
                 neighbourhood move operators to change the state of a
                 given solution. The proposed method is tested on
                 standard benchmark instances of two common VRP
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",

Genetic Programming entries for John H Drake Nikolaos Kililis Ender Ozcan