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

TL;DR

This paper introduces a hybrid estimation of distribution algorithm (HEDA) that combines EDA with simulated annealing to optimize joint stratification and sample allocation, outperforming existing methods in accuracy but with higher computational cost.

Contribution

The paper presents a novel hybrid EDA that improves optimization of stratification and sample allocation by integrating simulated annealing, achieving superior results over traditional algorithms.

Findings

HEDA achieves the best results on benchmark tests.

HEDA outperforms genetic and hill-climbing algorithms.

HEDA has higher execution times and computational costs.

Abstract

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.