Learning Initialisation Heuristic for Large Scale Vehicle Routing Problem with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @InProceedings{Cavalcanti-Costa:2021:CEC,
-
author = "Joao Guilherme {Cavalcanti Costa} and Yi Mei and
Mengjie Zhang",
-
booktitle = "2021 IEEE Congress on Evolutionary Computation (CEC)",
-
title = "Learning Initialisation Heuristic for Large Scale
Vehicle Routing Problem with Genetic Programming",
-
year = "2021",
-
editor = "Yew-Soon Ong",
-
pages = "1864--1871",
-
address = "Krakow, Poland",
-
month = "28 " # jun # "-1 " # jul,
-
keywords = "genetic algorithms, genetic programming, Measurement,
Space vehicles, Industries, NP-hard problem, Vehicle
routing, Search problems, Large Scale Vehicle Routing,
Hyper-Heuristic, Initialisation",
-
isbn13 = "978-1-7281-8393-0",
-
DOI = "doi:10.1109/CEC45853.2021.9504938",
-
abstract = "The Large Scale Vehicle Routing Problem is a classical
NP-hard problem. It has several applications in the
industry and has always been the focus of studies and
development of new, ever more complex, techniques to
solve it. An important group of these techniques are
Local Search-based, which are sensitive to the initial
solution given to them. However, finding effective
initial solutions is not a trivial task, requiring
domain knowledge for building them. Although some
Genetic Programming Hyper-Heuristics (GPHH) have tried
to build better heuristics automatically, they barely
give an advantage for improving the solution
afterwards. This paper aims to show that Genetic
Programming can identify better regions of the search
space, where the initial solutions can be improved more
efficiently with optimisation steps. This is done by
developing new terminals and a new fitness function,
which are based on the width of the routes, a metric
that was recently found to be an important feature for
good solutions. The obtained results show that the
proposed approach finds better final solutions than
when using classical initial heuristics or other GPHH,
for both time efficiency and effectiveness.",
-
notes = "Also known as \cite{9504938}",
- }
Genetic Programming entries for
Joao Guilherme Cavalcanti Costa
Yi Mei
Mengjie Zhang
Citations