A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem-Reference-Cited by-同舟云学术

A Learnheuristic Approach to A Constrained Multi-Objective Portfolio Optimisation Problem

Published:2023-04-23 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence
language:
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Author:

Bullah Sonia¹^ORCID,van Zyl Terence L.²^ORCID

Affiliation:

1. School of Computer Science and Applied Mathematics, University of the Witwatersrand, South Africa

2. Institute for Intelligent Systems, University of Johannesburg, South Africa

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3596947.3596965

Reference23 articles.

1. Portfolio Constraints: An Empirical Analysis

2. Pymoo: Multi-Objective Optimization in Python

3. Julian Blank and Kalyanmoy Deb. 2022. pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. arxiv:2204.05855 [cs.NE] Julian Blank and Kalyanmoy Deb. 2022. pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. arxiv:2204.05855 [cs.NE]

4. Julian Blank and Kalyanmoy Deb . 2022. pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. arXiv preprint arXiv:2204.05855 ( 2022 ). Julian Blank and Kalyanmoy Deb. 2022. pysamoo: Surrogate-Assisted Multi-Objective Optimization in Python. arXiv preprint arXiv:2204.05855 (2022).

5. Stephen Boyd , Enzo Busseti , Steve Diamond , Ronald N Kahn , Kwangmoo Koh , Peter Nystrup , Jan Speth , 2017. Multi-period trading via convex optimization. Foundations and Trends® in Optimization 3, 1 ( 2017 ), 1–76. Stephen Boyd, Enzo Busseti, Steve Diamond, Ronald N Kahn, Kwangmoo Koh, Peter Nystrup, Jan Speth, 2017. Multi-period trading via convex optimization. Foundations and Trends® in Optimization 3, 1 (2017), 1–76.

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