Quantum-inspired evolutionary algorithms for financial data analysis

Authors:
Kai Fan;Anthony Brabazon;Conall O'Sullivan;Michael O'Neill
Affiliations:
Natural Computing Research and Applications Group, University College Dublin, Ireland and School of Business, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland;School of Business, University College Dublin, Ireland;Natural Computing Research and Applications Group, University College Dublin, Ireland
Venue:
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Year:
2008

Citing 3
Cited 0

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Abstract

This paper describes a real-valued quantum-inspired evolutionary algorithm (QIEA), a new computational approach which bears similarity with estimation of distribution algorithms (EDAs). The study assesses the performance of the QIEA on a series of benchmark problems and compares the results with those from a canonical genetic algorithm. Furthermore, we apply QIEA to a finance problem, namely non-linear principal component analysis of implied volatilities. The results from the algorithm are shown to be robust and they suggest potential for useful application of the QIEA to high-dimensional optimization problems in finance.