We develop a flexible Gaussian Process (GP) framework for learning the
covariance structure of Age- and Year-specific mortality surfaces. Utilizing
the additive and multiplicative structure of GP kernels, we design a genetic
programming algorithm to search for the most expressive kernel for a given
population. Our compositional search builds off the Age-Period-Cohort (APC)
paradigm to construct a covariance prior best matching the spatio-temporal
dynamics of a mortality dataset. We apply the resulting genetic algorithm (GA)
on synthetic case studies to validate the ability of the GA to recover APC
structure, and on real-life national-level datasets from the Human Mortality
Database. Our machine-learning based analysis provides novel insight into the
presence/absence of Cohort effects in different populations, and into the
relative smoothness of mortality surfaces along the Age and Year dimensions.
Our modelling work is done with the PyTorch libraries in Python and provides an
in-depth investigation of employing GA to aid in compositional kernel search
for GP surrogates.

本文利用柔性高斯过程框架，设计了遗传编程算法来搜索适合于特定人口的最具表现性的内核。研究旨在揭示不同人口中队列效应有无及死亡率表面在年龄和年份维度上的相对平滑性。用编程算法对全国范围的真实数据集进行验证。