Portfolio management is a multi-period multi-objective optimisation problem
subject to a wide range of constraints. However, in practice, portfolio
management is treated as a single-period problem partly due to the
computationally burdensome hyper-parameter search procedure needed to construct
a multi-period Pareto frontier. This study presents the \gls{ParDen-Sur}
modelling framework to efficiently perform the required hyper-parameter search.
\gls{ParDen-Sur} extends previous surrogate frameworks by including a reservoir
sampling-based look-ahead mechanism for offspring generation in \glspl{EA}
alongside the traditional acceptance sampling scheme. We evaluate this
framework against, and in conjunction with, several seminal \gls{MO} \glspl{EA}
on two datasets for both the single- and multi-period use cases. Our results
show that \gls{ParDen-Sur} can speed up the exploration for optimal
hyper-parameters by almost $2\times$ with a statistically significant
improvement of the Pareto frontiers, across multiple \glspl{EA}, for both
datasets and use cases.

本文提出了使用 ParDen-Sur 建模框架来解决组合管理作为一个多期多目标优化问题中的超参数搜索问题，并验证 ParDen-Sur 可以显著提高 Pareto 前沿的多个 EA 在单期和多期使用情况下的性能表现。