EvoNum 2008

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The paper states two necessary conditions for an efficient and successful algorithm: (1) it must not converge on the slope of the fitness function, and (2) it must be allowed to converge in the valley. It also shows a simple Gaussian EDA with truncation selection which tries to fight the premature convergence by enlarging the ML estimate of standard deviation by a constant factor k. Finally, it is shown that a constant factor k that would satisfy the two stated requirements does not exist and that different factors for slope and for valley are needed.

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EvoNum 2008

  1. 1. Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress Petr Pošík Czech Technical University in Prague Faculty of Electrical Engineering Department of Cybernetics Intelligent Data Analysis Group P. Pošík c 2008 EvoWorkshops – EvoNUM, Napoli, 26.3.2008 – 1 / 21

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