# A Grouping Genetic Algorithm for Joint Stratification and Sample Allocation Designs

arXiv: https://arxiv.org/abs/1709.03076 · HTML: https://mlsanity.com/paper/1709.03076/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Combining K-means type algorithms with Hill Climbing for Joint Stratification and Sample Allocation Designs](https://mlsanity.com/paper/2108.08038/) (2021)
- [A hybrid estimation of distribution algorithm for joint stratification and sample allocation](https://mlsanity.com/paper/2201.04068/) (2022)

## Abstract (arXiv)

Predicting the cheapest sample size for the optimal stratification in multivariate survey design is a problem in cases where the population frame is large. A solution exists that iteratively searches for the minimum sample size necessary to meet accuracy constraints in partitions of atomic strata created by the Cartesian product of auxiliary variables into larger strata. The optimal stratification can be found by testing all possible partitions. However the number of possible partitions grows exponentially with the number of initial strata. There are alternative ways of modelling this problem, one of the most natural is using Genetic Algorithms (GA). These evolutionary algorithms use recombination, mutation and selection to search for optimal solutions. They often converge on optimal or near-optimal solution more quickly than exact methods. We propose a new GA approach to this problem using grouping genetic operators instead of traditional operators. The results show a significant improvement in solution quality for similar computational effort, corresponding to large monetary savings.