ParDen：代理辅助的超参数投资组合优化
ParDen: Surrogate Assisted Hyper-Parameter Optimisation for Portfolio Selection
摘要：
投资组合优化是一个多目标优化问题（MOP），投资者的目标是优化投资组合预期收益最大化同时风险和其他成本最小化的冲突标准。然而，选择投资组合是计算上昂贵的问题，因为与对测试数据执行多个评估（“回测”）而不是解决凸优化问题本身相关联的成本。在这项研究中，我们提出了ParDen，一种算法，包括任何歧视性或生成机器学习模型作为替代，以减轻计算昂贵的回测过程。此外，我们还比较了其他元启发式算法的性能：NSGA-II、R-NSGA-II、NSGA-III、R-NSGA-III、U-NSGA-III、MO-CMA-ES和COMO-CMA-ES。我们使用多目标性能指标来衡量性能，包括代际距离Plus、反向代际距离Plus和超级卷。我们还考虑了Hypervolume的元指标，成功率和平均执行成功率，以提供对解决方案质量的更深入了解。我们的研究结果表明，ParDen可以减少近三分之一的评估所需的数量，同时获得一个改进的帕累托前超过国家的最先进的投资组合选择的问题。
英文摘要：
Portfolio optimisation is a multi-objective optimisation problem (MOP), where an investor aims to optimise the conflicting criteria of maximising a portfolio's expected return whilst minimising its risk and other costs. However, selecting a portfolio is a computationally expensive problem because of the cost associated with performing multiple evaluations on test data ("backtesting") rather than solving the convex optimisation problem itself. In this research, we present ParDen, an algorithm for the inclusion of any discriminative or generative machine learning model as a surrogate to mitigate the computationally expensive backtest procedure. In addition, we compare the performance of alternative metaheuristic algorithms: NSGA-II, R-NSGA-II, NSGA-III, R-NSGA-III, U-NSGA-III, MO-CMA-ES, and COMO-CMA-ES. We measure performance using multi-objective performance indicators, including Generational Distance Plus, Inverted Generational Distance Plus and Hypervolume. We also consider meta-indicators, Success Rate and Average Executions to Success Rate, of the Hypervolume to provide more insight into the quality of solutions. Our results show that ParDen can reduce the number of evaluations required by almost a third while obtaining an improved Pareto front over the state-of-the-art for the problem of portfolio selection.
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