  In agent-based simulations, synthetic populations of agents are commonly used
to represent the structure, behaviour, and interactions of individuals.
However, generating a synthetic population that accurately reflects real
population statistics is a challenging task, particularly when performed at
scale. In this paper, we propose a multi objective combinatorial optimisation
technique for large scale population synthesis. We demonstrate the
effectiveness of our approach by generating a synthetic population for selected
regions and validating it on contingency tables from real population data. Our
approach supports complex hierarchical structures between individuals and
households, is scalable to large populations and achieves minimal contigency
table reconstruction error. Hence, it provides a useful tool for policymakers
and researchers for simulating the dynamics of complex populations.
