  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.
