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A Single Population Genetic Programming based Ensemble Learning Approach to Job Shop Scheduling

Authors:

John Park,

Su Nguyen,

Mengjie Zhang,

Mark JohnstonAuthors Info & Claims

GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 1451 - 1452

https://doi.org/10.1145/2739482.2764651

Published: 11 July 2015 Publication History

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Abstract

Genetic Programming based hyper-heuristics (GP-HH) for dynamic job shop scheduling (JSS) problems are approaches which aim to address the issue where heuristics are only effective for specific JSS problem domains, and that designing effective heuristics for JSS problems can be difficult. This paper is a preliminary investigation into improving the robustness of heuristics evolved by GP-HH by evolving ensembles of dispatching rules from a single population of GP individuals. The results show that the current approach does not evolve significantly better or more robust rules than a standard GP-HH approach of evolving single constituent rules.

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Cited By

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Xu MMei YZhang FZhang M(2024)Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and EnsemblesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.333462628:6(1761-1775)Online publication date: Dec-2024
https://doi.org/10.1109/TEVC.2023.3334626
Zhang FMei YNguyen SZhang M(2024)Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325524628:1(147-167)Online publication date: Feb-2024
https://doi.org/10.1109/TEVC.2023.3255246
Xu MZhang FMei YZhang M(2022)Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870340(01-08)Online publication date: 18-Jul-2022
https://doi.org/10.1109/CEC55065.2022.9870340
Show More Cited By

Index Terms

A Single Population Genetic Programming based Ensemble Learning Approach to Job Shop Scheduling
1. Applied computing
  1. Operations research
2. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Heuristic function construction
  2. Machine learning
    1. Machine learning algorithms
      1. Ensemble methods

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GECCO Companion '15: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1568 pages

ISBN:9781450334884

DOI:10.1145/2739482

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 11 July 2015

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SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Xu MMei YZhang FZhang M(2024)Genetic Programming for Dynamic Flexible Job Shop Scheduling: Evolution With Single Individuals and EnsemblesIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.333462628:6(1761-1775)Online publication date: Dec-2024
https://doi.org/10.1109/TEVC.2023.3334626
Zhang FMei YNguyen SZhang M(2024)Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop SchedulingIEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.325524628:1(147-167)Online publication date: Feb-2024
https://doi.org/10.1109/TEVC.2023.3255246
Xu MZhang FMei YZhang M(2022)Genetic Programming with Multi-case Fitness for Dynamic Flexible Job Shop Scheduling2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870340(01-08)Online publication date: 18-Jul-2022
https://doi.org/10.1109/CEC55065.2022.9870340
Huang ZMei YZhang M(2021)Investigation of Linear Genetic Programming for Dynamic Job Shop Scheduling2021 IEEE Symposium Series on Computational Intelligence (SSCI)10.1109/SSCI50451.2021.9660091(1-8)Online publication date: 5-Dec-2021
https://doi.org/10.1109/SSCI50451.2021.9660091
Hart ESim K(2018)On constructing ensembles for combinatorial optimisationEvolutionary Computation10.1162/evco_a_0020326:1(67-87)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1162/evco_a_00203
Nguyen SMei YZhang M(2017)Genetic programming for production scheduling: a survey with a unified frameworkComplex & Intelligent Systems10.1007/s40747-017-0036-x3:1(41-66)Online publication date: 24-Feb-2017
https://doi.org/10.1007/s40747-017-0036-x

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Genetic programming with diverse partner selection for dynamic flexible job shop scheduling

Feature Selection in Evolving Job Shop Dispatching Rules with Genetic Programming

Niching Genetic Programming based Hyper-heuristic Approach to Dynamic Job Shop Scheduling: An Investigation into Distance Metrics

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