PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

local and global oder 3/2 convergence of a surrogate evolutionary algorithm
Anne Auger, Marc Schoenauer and Olivier Teytaud
In: GECCO - genetic and evolution computation conference, 25 june 2005, Washington, USA.


A Quasi-Monte-Carlo method based on the computation of a surrogate model of the tness function is proposed, and its convergence at super-linear rate 3/2 is proved under rather mild assumptions on the tness function { but assuming that the starting point lies within a small neighborhood of a global maximum. A memetic algorithm is then constructed, that performs both a random exploration of the search space and the exploitation of the best-so-far points using the previous surrogate local algorithm, coupled through selection. Under the same mild hypotheses, the global convergence of the memetic algorithm, at the same 3/2 rate, is proved.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1753
Deposited By:Marc Schoenauer
Deposited On:28 November 2005