阻塞随机抽样与分布估计算法

Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) Pub Date : 2002-05-12 DOI:10.1109/CEC.2002.1004446

Roberto Santana, H. Mühlenbein

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引用次数: 10

摘要

对于优化问题，玻尔兹曼分布是一个很好的搜索分布。我们比较了两种近似玻尔兹曼分布的方法——分布估计算法(EDA)和马尔可夫链蒙特卡罗方法(MCMC)。结果表明，在二进制函数的空间中，即使是阻塞的MCMC方法也仅在一小类问题上优于EDA。在这些情况下，温度T = 0表现最佳。

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Blocked stochastic sampling versus Estimation of Distribution Algorithms

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.

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Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)

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