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Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting

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Genetic and Evolutionary Computation — GECCO 2003 (GECCO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2724))

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Abstract

This paper investigates the sample weighting effect on Genetic Parallel Programming (GPP) that evolves parallel programs to solve the training samples captured directly from a real-world system. The distribution of these samples can be extremely biased. Standard GPP assigns equal weights to all samples. It slows down evolution because crowded regions of samples dominate the fitness evaluation and cause premature convergence. This paper compares the performance of four sample weighting (SW) methods, namely, Equal SW (ESW), Class-equal SW (CSW), Static SW (SSW) and Dynamic SW (DSW) on five training sets. Experimental results show that DSW is superior in performance on tested problems.

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References

  1. Leung, K.S., Lee, K.H., Cheang, S.M.: Evolving Parallel Machine Programs for a Multi-ALU Processor. Proc. of IEEE Congress on Evolutionary Computation (2002) 1703–1708

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  2. Leung, K.S., Lee, K.H., Cheang, S.M.: Balancing Samples’ Contributions on GA Learning. Proc. of the 4th Int. Conf. on Evolvable Systems: From Biology to Hardware (ICES), Lecture Notes in Computer Science, Springs-Verlag (2001) 256–266

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  3. Leung, K.S., Lee, K.H., Cheang, S.M.: Parallel Programs are More Evolvable than Sequential Programs. Proc. of the 6th Euro. Conf. on Genetic Programming (EuroGP), Lecture Notes in Computer Science, Springs-Verlag (2003)

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong

    Sin Man Cheang, Kin Hong Lee & Kwong Sak Leung

Authors
  1. Sin Man Cheang
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  2. Kin Hong Lee
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  3. Kwong Sak Leung
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Editor information

Editors and Affiliations

  1. Center for Applied Scientific Computing (CASC), Lawrence Livermore National Laboratory, 7000 East Avenue, L-561, Livermore, CA, 94550, USA

    Erick Cantú-Paz

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© 2003 Springer-Verlag Berlin Heidelberg

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Cheang, S.M., Lee, K.H., Leung, K.S. (2003). Improving Evolvability of Genetic Parallel Programming Using Dynamic Sample Weighting. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45110-2_72

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  • DOI: https://doi.org/10.1007/3-540-45110-2_72

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40603-7

  • Online ISBN: 978-3-540-45110-5

  • eBook Packages: Springer Book Archive

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