Blocked Stochastic Sampling versus Estimation of Distribution Algorithms

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“Blocked Stochastic Sampling versus Estimation of Distribution Algorithms” by R. Santana and H. Mühlenbein. In Proceedings of the 2002 Congress on Evolutionary Computation CEC-2002, 2002, pp. 1390-1395.

Abstract

The Boltzmann distribution is a good candidate for a search distribution for optimization problems. We compare two methods to approximate the Boltzmann distribution-Estimation of Distribution Algorithms (EDA) and Markov Chain Monte Carlo methods (MCMC). It turns out that in the space of binary functions even blocked MCMC methodsoutperform EDA on a small class of problems only. In these cases a temperature of T=0 performed best

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BibTeX entry:

@inproceedings{Santana_and_Muehlenbein:2002,
   author = {R. Santana and H. M{\"u}hlenbein},
   title = {Blocked Stochastic Sampling versus {E}stimation of
	{D}istribution {A}lgorithms},
   booktitle = {Proceedings of the 2002 Congress on Evolutionary
	Computation CEC-2002},
   volume = {2},
   pages = {1390-1395},
   publisher = {IEEE press},
   year = {2002}
}

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