A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Kevin Sim and Emma Hart",
  title =        "A Combined Generative and Selective Hyper-heuristic
                 for the Vehicle Routing Problem",
  booktitle =    "GECCO '16: Proceedings of the 2016 Annual Conference
                 on Genetic and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "1093--1100",
  keywords =     "genetic algorithms, genetic programming",
  month =        "20-24 " # jul,
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4206-3",
  DOI =          "doi:10.1145/2908812.2908942",
  abstract =     "Hyper-heuristic methods for solving vehicle routing
                 problems (VRP) have proved promising on a range of
                 data. The vast majority of approaches apply selective
                 hyper-heuristic methods that iteratively choose
                 appropriate heuristics from a fixed set of pre-defined
                 low-level heuristics to either build or perturb a
                 candidate solution. We propose a novel hyper-heuristic
                 called GP-MHH that operates in two stages. The first
                 stage uses a novel Genetic Programming (GP) approach to
                 evolve high quality constructive heuristics; these can
                 be used with any existing method that relies on a
                 candidate solution(s) as its starting point. In the
                 second stage, a perturbative hyper-heuristic is applied
                 to candidate solutions created from the new heuristics.
                 The new constructive heuristics are shown to outperform
                 existing low-level heuristics. When combined with a
                 naive perturbative hyper-heuristic they provide results
                 which are both competitive with known optimal values
                 and outperform a recent method that also designs new
                 heuristics on some standard benchmarks. Finally, we
                 provide results on a set of rich VRPs, showing the
                 generality of the approach.",
  notes =        "GECCO-2016 A Recombination of the 25th International
                 Conference on Genetic Algorithms (ICGA-2016) and the
                 21st Annual Genetic Programming Conference (GP-2016)",

Genetic Programming entries for Kevin Sim Emma Hart