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General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms

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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 6238))

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Abstract

We present new methods for the running time analysis of parallel evolutionary algorithms with spatially structured populations. These methods are applied to estimate the speed-up gained by parallelization in pseudo-Boolean optimization. The possible speed-up increases with the density of the topology. Surprisingly, even sparse topologies like ring graphs lead to a significant speed-up for many functions while not increasing the total number of function evaluations. We also give practical hints towards choosing the minimum number of processors that gives an optimal speed-up.

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References

  1. Nedjah, N., de Macedo Mourelle, L.: Parallel Evolutionary Computations. Springer, Heidelberg (2006)

    Book  MATH  Google Scholar 

  2. Tomassini, M.: Spatially Structured Evolutionary Algorithms: Artificial Evolution in Space and Time. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  3. Cantú Paz, E.: A survey of parallel genetic algorithms. Technical report, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana Champaign, Urbana, IL (1997)

    Google Scholar 

  4. Rudolph, G.: Takeover time in parallel populations with migration. In: BIOMA 2006, pp. 63–72 (2006)

    Google Scholar 

  5. Wegener, I.: Methods for the analysis of evolutionary algorithms on pseudo-Boolean functions. In: Sarker, R., Yao, X., Mohammadian, M. (eds.) Evolutionary Optimization, pp. 349–369. Kluwer, Dordrecht (2002)

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  6. Lässig, J., Sudholt, D.: The benefit of migration in parallel evolutionary algorithms. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2010, pp. 1105–1112. ACM, New York (2010)

    Google Scholar 

  7. Droste, S., Jansen, T., Wegener, I.: On the analysis of the (1+1) evolutionary algorithm. Theoretical Computer Science 276, 51–81 (2002)

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  8. He, J., Yao, X.: A study of drift analysis for estimating computation time of evolutionary algorithms. Natural Computing 3(1), 21–35 (2004)

    Article  MathSciNet  MATH  Google Scholar 

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Lässig, J., Sudholt, D. (2010). General Scheme for Analyzing Running Times of Parallel Evolutionary Algorithms. 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 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_24

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  • DOI: https://doi.org/10.1007/978-3-642-15844-5_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15843-8

  • Online ISBN: 978-3-642-15844-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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