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Parallel Evolutionary Algorithms Performing Pairwise Comparisons

Abstract : We study mathematically and experimentally the conver-gence rate of differential evolution and particle swarm opti-mization for simple unimodal functions. Due to paralleliza-tion concerns, the focus is on lower bounds on the runtime, i.e upper bounds on the speed-up, as a function of the pop-ulation size. Two cases are particularly relevant: A popula-tion size of the same order of magnitude as the dimension and larger population sizes. We use the branching factor as a tool for proving bounds and get, as upper bounds, a lin-ear speed-up for a population size similar to the dimension, and a logarithmic speed-up for larger population sizes. We then propose parametrizations for differential evolution and particle swarm optimization that reach these bounds.
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Contributor : Olivier Teytaud Connect in order to contact the contributor
Submitted on : Monday, November 3, 2014 - 1:59:51 AM
Last modification on : Saturday, June 25, 2022 - 10:14:35 PM
Long-term archiving on: : Wednesday, February 4, 2015 - 10:06:45 AM


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  • HAL Id : hal-01077626, version 1


Marie-Liesse Cauwet, Olivier Teytaud, Shih-Yuan Chiu, Kuo-Min Lin, Shi-Jim Yen, et al.. Parallel Evolutionary Algorithms Performing Pairwise Comparisons. Foundations of Genetic Algorithms, 2015, Aberythswyth, United Kingdom. pp.99-113. ⟨hal-01077626⟩



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