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A Weighted Local Sharing Technique for Multimodal Optimisation

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Simulated Evolution and Learning (SEAL 2008)

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

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Abstract

Local sharing is a method designed for efficient multimodal optimisation that combines fitness sharing, spatially-structured populations and elitist replacement. In local sharing the bias toward sharing or spatial effect is controlled by the deme (neighbourhood) size. This introduces an undesirable trade-off; to maximise the sharing effect, deme sizes must be large, but the opposite must be true if one wishes to maximise the influence of spatial population structure. This paper introduces a modification to the local sharing method whereby parent selection and fitness sharing operate at two different spatial levels; parent selection is performed within small demes, while the effect of fitness sharing is weighted according to the distances between individuals in the population structure. The proposed method, as tested on several benchmark problems, demonstrates a level of efficiency and parameter robustness that surpasses the basic local sharing method.

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Author information

Authors and Affiliations

  1. Department of Information Science, University of Otago, Dunedin, New Zealand

    Grant Dick & Peter A. Whigham

Authors
  1. Grant Dick
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  2. Peter A. Whigham
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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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Dick, G., Whigham, P.A. (2008). A Weighted Local Sharing Technique for Multimodal Optimisation. 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_46

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_46

  • 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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