A multi-objective evolutionary algorithm for portfolio optimisation
Bradshaw, Noel-Ann, Walshaw, Chris, 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 1902956761Full text not available from this repository.
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)  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:|| 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
|Last Modified:||14 Oct 2016 09:18|
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