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GPGPU-Compatible Archive Based Stochastic Ranking Evolutionary Algorithm (G-ASREA) for Multi-Objective Optimization

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Parallel Problem Solving from Nature, PPSN XI (PPSN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6239))

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Abstract

In this paper, a GPGPU (general purpose graphics processing unit) compatible Archived based Stochastic Ranking Evolutionary Algorithm (G-ASREA) is proposed, that ranks the population with respect to an archive of non-dominated solutions. It reduces the complexity of the deterministic ranking operator from O(mn 2) to O(man) and further speeds up ranking on GPU.

Experiments compare G-ASREA with a CPU version of ASREA and NSGA-II on ZDT test functions for a wide range of population sizes. The results confirm the gain in ranking complexity by showing that on 10K individuals, G-ASREA ranking is ≈ ×5000 faster than NSGA-II and ≈ ×15 faster than ASREA.

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Author information

Authors and Affiliations

  1. LogXlabs Research Center, Paris, France

    Deepak Sharma

  2. FDBT, LSIIT, Université de Strasbourg, France

    Pierre Collet

Authors
  1. Deepak Sharma
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  2. Pierre Collet
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of ComputerScience, AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Robert Schaefer

  2. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  3. Department of Mathematics and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309, Bielsko-Biala, Poland

    Joanna Kołodziej

  4. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

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Sharma, D., Collet, P. (2010). GPGPU-Compatible Archive Based Stochastic Ranking Evolutionary Algorithm (G-ASREA) for Multi-Objective Optimization. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_12

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  • DOI: https://doi.org/10.1007/978-3-642-15871-1_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15870-4

  • Online ISBN: 978-3-642-15871-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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