{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pareto-driven-surrogate-parden-sur-assisted","title":"Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations","arxiv_id":"2209.13528","date":"2022-09-13","proceeding":null,"authors":["Terence L. Van Zyl","Matthew Woolway","Andrew Paskaramoorthy"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2209.13528v1","url_pdf":"https://arxiv.org/pdf/2209.13528v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pareto-driven-surrogate-parden-sur-assisted","repo_url":"https://github.com/intelligent-systems-modelling/surrogate-assisted-moo-cvxport","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}