# 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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