Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Nguyen:2016:ieeeTCYB,
-
author = "Su Nguyen and Mengjie Zhang and Kay Chen Tan",
-
journal = "IEEE Transactions on Cybernetics",
-
title = "Surrogate-Assisted Genetic Programming With Simplified
Models for Automated Design of Dispatching Rules",
-
year = "2017",
-
volume = "47",
-
number = "9",
-
pages = "2951--2965",
-
month = sep,
-
keywords = "genetic algorithms, genetic programming, evolutionary
design, hyper-heuristic, scheduling",
-
ISSN = "2168-2267",
-
DOI = "doi:10.1109/TCYB.2016.2562674",
-
size = "15 pages",
-
abstract = "Automated design of dispatching rules for production
systems has been an interesting research topic over the
last several years. Machine learning, especially
genetic programming (GP), has been a powerful approach
to dealing with this design problem. However, intensive
computational requirements, accuracy and
interpretability are still its limitations. This paper
aims at developing a new surrogate assisted GP to help
improving the quality of the evolved rules without
significant computational costs. The experiments have
verified the effectiveness and efficiency of the
proposed algorithms as compared to those in the
literature. Furthermore, new simplification and
visualisation approaches have also been developed to
improve the interpretability of the evolved rules.
These approaches have shown great potentials and proved
to be a critical part of the automated design system.",
-
notes = "Also known as \cite{7473913}",
- }
Genetic Programming entries for
Su Nguyen
Mengjie Zhang
Kay Chen Tan
Citations