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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2008: Simulated Evolution and Learning pp 220–228Cite as

Extremal Optimisation and Bin Packing

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

Abstract

Extremal Optimisation (EO) is a fairly new entrant into the realms of stochastic based optimisation techniques. Its behaviour differs from other more common algorithms as it alters a poorly performing part of the one solution used without regard to the effect this will have on the quality of the solution. While this means that its performance on assignment problems may be poor if used on its own, this same ‘failing’ makes it a very suitable base for a meta-heuristic. An analysis of the performance of naive EO on the classic bin packing problem is performed in this paper. Results are also presented that show that the same naive EO can be used in a meta-heuristic that performs very well.

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Author information

Authors and Affiliations

  1. Faculty of Information and Communication Technologies, Swinburne University of Technology, Victoria, Australia

    Tim Hendtlass

  2. School of Information Technology, Bond University, Queensland, Australia

    Marcus Randall

Authors
  1. Tim Hendtlass
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  2. Marcus Randall
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Cite this paper

Hendtlass, T., Randall, M. (2008). Extremal Optimisation and Bin Packing. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_23

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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