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.
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Fan, K., Brabazon, A., O’Sullivan, C., O’Neill, M. (2008). Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_14
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DOI: https://doi.org/10.1007/978-3-540-78761-7_14
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