A Combined Generative and Selective Hyper-heuristic for the Vehicle Routing Problem
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Sim:2016:GECCO,
-
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
Citations