Mei 2016 Feature selection in evolving job shop dispatching rules with.pdf (483.31 kB)

Feature selection in evolving job shop dispatching rules with genetic programming

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conference contribution
posted on 31.03.2021, 03:11 by Yi Mei, Mengjie Zhang, Su Nyugen
Genetic Programming (GP) has been successfully used to automatically design dispatching rules in job shop scheduling. The goal of GP is to evolve a priority function that will be used to order the waiting jobs at each decision point, and decide the next job to be processed. To this end, the proper terminals (i.e. job shop features) have to be decided. When evolving the priority function, various job shop features can be included in the terminal set. However, not all the features are helpful, and some features are irrelevant to the rule. Including irrelevant features into the terminal set enlarges the search space, and makes it harder to achieve promising areas. Thus, it is important to identify the important features and remove the irrelevant ones to improve the GP-evolved rules. This paper proposes a domain-knowledge-free feature ranking and selection approach. As a result, the terminal set is significantly reduced and only the most important features are selected. The experimental results show that using only the selected features can lead to significantly better GP-evolved rules on both training and unseen test instances. © Mei 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in 'GECCO '16: Proceedings of the Genetic and Evolutionary Computation Conference', https://doi.org/10.1145/2908812.2908822.

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Preferred citation

Mei, Y., Zhang, M. & Nyugen, S. (2016, January). Feature selection in evolving job shop dispatching rules with genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) GECCO '16: Genetic and Evolutionary Computation Conference, ACM (pp. 365-372). ACM. https://doi.org/10.1145/2908812.2908822

Conference name

GECCO '16: Genetic and Evolutionary Computation Conference

Conference Place

ACM

Conference start date

20/07/2016

Conference finish date

24/07/2016

Title of proceedings

Proceedings of the Genetic and Evolutionary Computation Conference (GECCO)

Contribution type

Published Paper

Publication or Presentation Year

01/01/2016

Pagination

365-372

Publisher

ACM

Publication status

Published

Exports