Genetic programming with local search to evolve priority rules for scheduling jobs on a machine with time-varying capacity
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{GILGALA:2021:SEC,
-
author = "Francisco J. Gil-Gala and Maria R. Sierra and
Carlos Mencia and Ramiro Varela",
-
title = "Genetic programming with local search to evolve
priority rules for scheduling jobs on a machine with
time-varying capacity",
-
journal = "Swarm and Evolutionary Computation",
-
year = "2021",
-
volume = "66",
-
pages = "100944",
-
month = oct,
-
note = "Special Issue on Memetic Computing: Accelerating
Optimization Heuristics with Problem-Dependent Local
Search Methods",
-
keywords = "genetic algorithms, genetic programming, One machine
scheduling, Priority rules, Local search, Memetic
algorithm",
-
ISSN = "2210-6502",
-
URL = "https://www.sciencedirect.com/science/article/pii/S2210650221001061",
-
DOI = "doi:10.1016/j.swevo.2021.100944",
-
size = "13 pages",
-
abstract = "Priority rules combined with schedule generation
schemes are a usual approach to online scheduling.
These rules are commonly designed by experts on the
problem domain. However, some automatic method may be
better as it could capture some characteristics of the
problem that are not evident to the human eye.
Furthermore, automatic methods could devise priority
rules adapted to particular sets of instances of the
problem at hand. In this paper we propose a Memetic
Algorithm, which combines a Genetic Program and a Local
Search algorithm, to evolve priority rules for the
problem of scheduling a set of jobs on a machine with
time-varying capacity. We propose a number of
neighbourhood structures that are specifically designed
to this problem. These structures were analyzed
theoretically and also experimentally on the version of
the problem with tardiness minimization, which provided
interesting insights on this problem. The results of
the experimental study show that a proper selection and
combination of neighbourhood structures allows the
Memetic Algorithm to outperform previous approaches to
the same problem",
-
notes = "Department of Computer Science, University of Oviedo,
Gijon 33204, Spain",
- }
Genetic Programming entries for
Francisco Javier Gil Gala
Maria Rita Sierra Sanchez
Carlos Mencia Cascallana
Ramiro Varela Arias
Citations