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An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming

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posted on 19.03.2021, 01:39 by Yi Mei, Su Nguyen, Bing Xue, Mengjie Zhang
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 10% 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. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Preferred citation

Mei, Y., Nguyen, S., Xue, B. & Zhang, M. (2017). An efficient feature selection algorithm for evolving job shop scheduling rules with genetic programming. IEEE Transactions on Emerging Topics in Computational Intelligence, 1(5), 339-353. https://doi.org/10.1109/TETCI.2017.2743758

Journal title

IEEE Transactions on Emerging Topics in Computational Intelligence

Volume

1

Issue

5

Publication date

01/01/2017

Pagination

339-353

Publisher

IEEE

Publication status

Published online

Contribution type

Article

Online publication date

21/09/2017

ISSN

2471-285X

eISSN

2471-285X

Article number

5

Exports

Journal articles

Exports