A new adaptive Boltzmann selection schedule SDS
A new adaptive Boltzmann selection schedule SDS
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一种新的自适应玻尔兹曼选择表 SDS
DOI：
10.1109/cec.2001.934388
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
H. Mühlenbein
中科院分区：
文献类型：
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作者：
T. Mahnig;H. Mühlenbein
因子分布算法（FDA）是一种利用分布将突变和重组结合起来的进化算法。分布是从一组选定的点估计出来的。然后，它被用来为下一代生成新的点。一般来说，为n个二进制变量定义的分布有2/sup n/个参数。因此计算起来太昂贵了。对于加性分解离散函数，存在一种将分布分解为条件分布和边际分布的算法，每一个都可以在多项式时间内计算。我们已经证明了FDA的收敛定理，但它只有在使用玻尔兹曼选择时才有效。由于缺乏良好的退火程序，玻尔兹曼选择在实践中没有使用。利用玻尔兹曼分布的平均适应度的泰勒展开，我们开发了一种称为SDS（标准差计划）的自适应退火计划，并在本工作中介绍。逆温度p的变化与标准差成反比。
The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm that combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. It is then used to generate new points for the next generation. In general a distribution defined for n binary variables has 2/sup n/ parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exists an algorithm that factors the distribution into conditional and marginal distributions, each of which can be computed in polynomial time. We have shown a convergence theorem for the FDA, but it is only valid using Boltzmann selection. Boltzmann selection was not used in practice because a good annealing schedule was lacking. Using a Taylor expansion of the average fitness of the Boltzmann distribution, we have developed an adaptive annealing schedule called SDS (Standard Deviation Schedule) that is introduced in this work. The inverse temperature p is changed inversely proportional to the standard deviation.