Metaheuristics and machine learning for joint stratification and sample allocation in survey design
Loading...
Files
Full Text E-thesis
Date
2022-01
Authors
O'Luing, Mervyn
Journal Title
Journal ISSN
Volume Title
Publisher
University College Cork
Published Version
Abstract
In this thesis, we propose a number of metaheuristics and machine learning
techniques to solve the joint stratification and sample allocation problem. Finding
the optimal solution to this problem is hard when the sampling frame is large, and
the evaluation algorithm is computationally burdensome.
To advance the research in this area, we explore and evaluate different
algorithmic methods of modelling and solving this problem. Firstly, we propose
a new genetic algorithm approach using "grouping" genetic operators instead of
traditional operators. Experiments show a significant improvement in solution
quality for similar computational effort. Next, we combine the capability of a
simulated annealing algorithm to escape from local minima with delta evaluation to
exploit the similarity between consecutive solutions and thereby reduce evaluation
time. Comparisons with two recent algorithms show the simulated annealing
algorithm attaining comparable solution qualities in less computation time.
Then, we consider the combination of the k-means and clustering algorithms
with a hill climbing algorithm in stages and report the solution costs, evaluation times
and training times. The multi-stage combinations generally compare well with recent
algorithms, and provide the survey designer with a greater choice of algorithms to
choose from.
Finally, we combine the explorative properties of an estimation of distribution
algorithm (EDA) to model the probabilities of an atomic stratum belonging to
different strata with the exploitative search properties of a simulated annealing
algorithm to create a hybrid estimation of distribution algorithm (HEDA).
Results of comparisons with the best solution qualities from our earlier
experiments show that the HEDA finds better solution qualities, but requires a longer
total execution time than alternative approaches we considered.
Description
Keywords
Metaheuristics , Machine learning , Stratification and sample allocation , Survey design
Citation
O'Luing, M. 2022. Metaheuristics and machine learning for joint stratification and sample allocation in survey design. PhD Thesis, University College Cork.