论文标题
关节分层和样品分配的分布算法的混合估计
A hybrid estimation of distribution algorithm for joint stratification and sample allocation
论文作者
论文摘要
在这项研究中，我们提出了分布算法（HEDA）的混合估计，以解决关节分层和样本分配问题。这是一个复杂的问题，其中每个可能分层集的每个分层的质量都可以测量其最佳样本分配。 EDA是随机黑盒优化算法，可用于搜索最佳分层时估算，构建和采样概率模型。在本文中，我们通过添加模拟退火算法使其成为混合EDA来增强EDA的开发特性。原子和连续地层的经验比较结果表明，与使用分组遗传算法对基准测试相比，HEDA达到到目前为止发现的最佳结果，模拟退火算法或爬山算法。但是，通常，HEDA的执行时间和总执行时间更高。
In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem. This is a complex problem in which each the quality of each stratification from the set of all possible stratifications is measured its optimal sample allocation. EDAs are stochastic black-box optimization algorithms which can be used to estimate, build and sample probability models in the search for an optimal stratification. In this paper we enhance the exploitation properties of the EDA by adding a simulated annealing algorithm to make it a hybrid EDA. Results of empirical comparisons for atomic and continuous strata show that the HEDA attains the bests results found so far when compared to benchmark tests on the same data using a grouping genetic algorithm, simulated annealing algorithm or hill-climbing algorithm. However, the execution times and total execution are, in general, higher for the HEDA.
