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International Conference on Parallel Problem Solving from Nature

PPSN 1998: Parallel Problem Solving from Nature — PPSN V pp 315–324Cite as

Further experimentations on the scalability of the GEMGA

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

Abstract

This paper reports the recent developments of the Gene Expression Messy Genetic Algorithm (GEMGA) research. It presents extensive experimental results for large problems with massive multi-modality, non-uniform scaling, and overlapping sub-problems. All the experimental results corroborate the linear time performance of the GEMGA for a wide range of problems, that can be decomposed into smaller overlapping and non-overlapping sub-problems in the chosen representation. These results further support the scalable performance of the GEMGA.

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Computer Science, Washington State University, 99164-2752, Pullman, WA, USA

    Hillol Kargupta

  2. Machine Intelligence Unit, Indian Statistical Institute, Calcutta, India

    Sanghamitra Bandyopadhyay

Authors
  1. Hillol Kargupta
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  2. Sanghamitra Bandyopadhyay
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Kargupta, H., Bandyopadhyay, S. (1998). Further experimentations on the scalability of the GEMGA. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056874

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  • DOI: https://doi.org/10.1007/BFb0056874

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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