Evolving Ensembles of Dispatching Rules using Genetic Programming for Job Shop Scheduling
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Park:2015:EuroGP,
-
author = "John Park and Su Nguyen and Mengjie Zhang and
Mark Johnston",
-
title = "Evolving Ensembles of Dispatching Rules using Genetic
Programming for Job Shop Scheduling",
-
booktitle = "18th European Conference on Genetic Programming",
-
year = "2015",
-
editor = "Penousal Machado and Malcolm I. Heywood and
James McDermott and Mauro Castelli and
Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
-
series = "LNCS",
-
volume = "9025",
-
publisher = "Springer",
-
pages = "92--104",
-
address = "Copenhagen",
-
month = "8-10 " # apr,
-
organisation = "EvoStar",
-
keywords = "genetic algorithms, genetic programming, Job shop
scheduling, Hyper-heuristics, Ensemble learning,
Cooperative coevolution, Robustness, Dispatching rules,
Combinatorial optimisation, Evolutionary computation",
-
isbn13 = "978-3-319-16500-4",
-
DOI = "doi:10.1007/978-3-319-16501-1_8",
-
abstract = "Job shop scheduling (JSS) problems are important
optimisation problems that have been studied
extensively in the literature due to their
applicability and computational difficulty. This paper
considers static JSS problems with makespan
minimisation, which are NP-complete for more than two
machines. Because finding optimal solutions can be
difficult for large problem instances, many heuristic
approaches have been proposed in the literature.
However, designing effective heuristics for different
JSS problem domains is difficult. As a result,
hyper-heuristics (HHs) have been proposed as an
approach to automating the design of heuristics. The
evolved heuristics have mainly been priority based
dispatching rules (DRs). To improve the robustness of
evolved heuristics generated by HHs, this paper
proposes a new approach where an ensemble of rules are
evolved using Genetic Programming (GP) and cooperative
coevolution, denoted as Ensemble Genetic Programming
for Job Shop Scheduling (EGP-JSS). The results show
that EGP-JSS generally produces more robust rules than
the single rule GP.",
-
notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in
conjunction with EvoCOP2015, EvoMusArt2015 and
EvoApplications2015",
- }
Genetic Programming entries for
John Park
Su Nguyen
Mengjie Zhang
Mark Johnston
Citations