Blocked Stochastic sampling versus estimation of distribution algorithms

被引：0
|
作者：
Santana, R [1 ]
Mühlenbein, H [1 ]
机构：
[1] ICIMAF, CP-10400 Havana, Cuba
来源：
CEC'02: PROCEEDINGS OF THE 2002 CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1 AND 2 | 2002年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The Boltzmann distribution is a good candidate for a search distribution for optimization problems. In this paper we compare two methods to approximate the Boltzmann distribution-Estimation of Distribution Algorithm's (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.
引用
收藏
页码：1390 / 1395
页数：6
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