Blocked stochastic sampling versus Estimation of Distribution Algorithms

Authors:
R. Santana;H. Muhlenbein
Affiliations:
ICIMAF, Havana, Cuba;Dept. of Inf. Eng., Feng China Univ., Taichung, Taiwan
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

Citing 0
Cited 3

Estimation of Distribution Algorithms with Kikuchi Approximations

Evolutionary Computation
Modeling and simulating complexity for discrete graphic Markov models: an experimental study

Math'04 Proceedings of the 5th WSEAS International Conference on Applied Mathematics
Univariate marginal distribution algorithm dynamics for a class of parametric functions with unitation constraints

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

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 methods outperform EDA on a small class of problems only. In these cases a temperature of T = 0 performed best.