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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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
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