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.

翻译：在这项研究中,我们建议对分配算法(HEDA)进行混合估计,以解决联合分层和抽样分配问题。这是一个复杂的问题,对一组所有可能的分层的每个分层的质量进行衡量,以衡量其最佳的抽样分配。EDA是随机黑箱优化算法,可用于估算、构建和抽样概率模型,以寻找最佳分层。在本文中,我们增加了模拟肛门算法,使其成为混合的分层。对原子和连续层进行的经验性比较结果表明,HEDA取得了迄今所发现的最佳结果,而与使用组合基因算法、模拟排泄算法或山坡算法对同一数据的基准测试相比,HEDA的总执行时间和总执行时间一般更高。