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Evolutionary Learning Based Iterated Local Search for Google Machine Reassignment Problems

  • Ayad TurkyEmail author
  • Nasser R. Sabar
  • Abdul Sattar
  • Andy Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10593)

Abstract

Iterated Local Search (ILS) is a simple yet powerful optimisation method that iteratively invokes a local search procedure with renewed starting points by perturbation. Due to the complexity of search landscape, different ILS strategies may better suit different problem instances or different search stages. To address this issue, this work proposes a new ILS framework which selects the most suited components of ILS based on evolutionary meta-learning. It has three additional components other than ILS: meta-feature extraction, meta-learning and classification. The meta-feature and meta-learning steps are to generate a multi-class classifier by training on a set of existing problem instances. The generated classifier then selects the most suitable ILS setting when performing on new instances. The classifier is generated by Genetic Programming. The effectiveness of the proposed ILS framework is demonstrated on the Google Machine Reassignment Problem. Experimental results show that the proposed framework is highly competitive compared to 10 state-of-the-art methods reported in the literature.

Keywords

Iterated Local Search Meta-learning Google machine reassignment problem Genetic programming 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ayad Turky
    • 1
    Email author
  • Nasser R. Sabar
    • 2
  • Abdul Sattar
    • 3
  • Andy Song
    • 1
  1. 1.School of Computer Science and I.T.RMIT UniversityMelbourneAustralia
  2. 2.Queensland University of TechnologyBrisbaneAustralia
  3. 3.Griffith UniversityNathanAustralia

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