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A multi-objective evolutionary algorithm for portfolio optimisation

A multi-objective evolutionary algorithm for portfolio optimisation

Bradshaw, Noel-Ann, Walshaw, Chris ORCID: 0000-0003-0253-7779, Ierotheou, Constantinos and Parrott, A. Kevin (2009) A multi-objective evolutionary algorithm for portfolio optimisation. In: Proceedings from Artificial Intelligence and Simulation of Behaviour Symposium 2009 on Evolutionary Systems. The Society for the Study of Artificial Intelligence and Simulation of Behaviour, London, UK, pp. 27-32. ISBN 1902956761

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Abstract

The use of heuristic evolutionary algorithms to address the problem of portfolio optimisation has been well documented. In order to decide which assets to invest in and how much to invest, one needs to assess the potential risk and return of different portfolios. This problem is ideal for solving using a Multi-Objective Evolutionary Algorithm (MOEA) that maximises return and minimises risk. We are working on a new MOEA loosely based on Zitzler's Strength Pareto Evolutionary Algorithm (SPEA2) [20] using Value at Risk (VaR) as the risk constraint. This algorithm currently uses a dynamic population in order to overcome the problem of loosing solutions. We are also investigating a dynamic diversity and density operator.

Item Type: Conference Proceedings
Title of Proceedings: Proceedings from Artificial Intelligence and Simulation of Behaviour Symposium 2009 on Evolutionary Systems
Additional Information: [1] This paper was presented and published in the Proceedings of the Symposium - Evolutionary Systems: a symposium at the AISB 2009 Convention (6-9 April 2009) Heriot-Watt University, Edinburgh, Scotland.
Uncontrolled Keywords: multi-objective optimisation, algorithm, evolutionary
Subjects: Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
School of Computing & Mathematical Sciences > Department of Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:18
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
URI: http://gala.gre.ac.uk/id/eprint/7104

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