An Efficient Feature Selection Algorithm for Evolving Job Shop Scheduling Rules With Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7975
- @Article{Mei:2017:ieeeETCI,
-
author = "Yi Mei and Su Nguyen and Bing Xue and Mengjie Zhang",
-
journal = "IEEE Transactions on Emerging Topics in Computational
Intelligence",
-
title = "An Efficient Feature Selection Algorithm for Evolving
Job Shop Scheduling Rules With Genetic Programming",
-
year = "2017",
-
volume = "1",
-
number = "5",
-
pages = "339--353",
-
month = oct,
-
keywords = "genetic algorithms, genetic programming, Feature
selection, hyper-heuristic, job shop scheduling",
-
DOI = "doi:10.1109/TETCI.2017.2743758",
-
abstract = "Automated design of job shop scheduling rules using
genetic programming as a hyper-heuristic is an emerging
topic that has become more and more popular in recent
years. For evolving dispatching rules, feature
selection is an important issue for deciding the
terminal set of genetic programming. There can be a
large number of features, whose importance/relevance
varies from one to another. It has been shown that
using a promising feature subset can lead to a
significant improvement over using all the features.
However, the existing feature selection algorithm for
job shop scheduling is too slow and inapplicable in
practice. In this paper, we propose the first practical
feature selection algorithm for job shop scheduling.
Our contributions are twofold. First, we develop a
Niching-based search framework for extracting a diverse
set of good rules. Second, we reduce the complexity of
fitness evaluation by using a surrogate model. As a
result, the proposed feature selection algorithm is
very efficient. The experimental studies show that it
takes less than 10percent of the training time of the
standard genetic programming training process, and can
obtain much better feature subsets than the entire
feature set. Furthermore, it can find better feature
subsets than the best-so-far feature subset.",
-
notes = "Also known as \cite{8048081}",
- }
Genetic Programming entries for
Yi Mei
Su Nguyen
Bing Xue
Mengjie Zhang
Citations