Name
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. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/CEC.2002.1004446 | CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 |
Keywords | DocType | Volume |
Markov processes,Monte Carlo methods,evolutionary computation,sampling methods,search problems,Boltzmann distribution,Estimation of Distribution Algorithms,Markov Chain Monte Carlo methods,binary functions,blocked stochastic sampling,optimization problems,search distribution | Conference | 2 |
ISBN | Citations | PageRank |
0-7803-7282-4 | 4 | 0.62 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Roberto Santana | 1 | 357 | 19.04 |
| Muhlenbein, H. | 2 | 4 | 0.62 |