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In this study we propose a hybrid estimation of distribution algorithm (HEDA)to solve the joint stratification and sample allocation problem. This is acomplex problem in which each the quality of each stratification from the setof all possible stratifications is measured its optimal sample allocation. EDAsare stochastic black-box optimization algorithms which can be used to estimate,build and sample probability models in the search for an optimalstratification. In this paper we enhance the exploitation properties of the EDAby adding a simulated annealing algorithm to make it a hybrid EDA. Results ofempirical comparisons for atomic and continuous strata show that the HEDAattains the bests results found so far when compared to benchmark tests on thesame data using a grouping genetic algorithm, simulated annealing algorithm orhill-climbing algorithm. However, the execution times and total execution are,in general, higher for the HEDA.

A hybrid estimation of distribution algorithm for joint stratification and sample allocation