Abstract
Commonly used metaheuristic optimisation techniques imbed stochastic elements into the selection of the initial population or/and into the solution-search strategy. Introducing randomness is often a means of escaping from local optima when searching for the global solution. However, depending on the ruggedness of the optimisation landscape and the complexity of the problem at hand, this practice leads to a dispersion of the reported solutions. Instead of relying on the best solution found in a set of runs, as is typical in many optimisation exercises, it is essential to get an indication of the expected dispersion of results by estimating the probability of converging to a “good” solution after a certain number of generations. We apply a range of statistical techniques for estimating the success probability and the convergence rate of popular evolutionary optimisation heuristics in the context of portfolio management. We show how this information can be utilised by a researcher to obtain a deeper understanding of algorithmic behaviour and to evaluate the relative performance of competitive optimisation schemes.
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Thomaidis, N.S., Vassiliadis, V. (2013). Stochastic Convergence Analysis of Metaheuristic Optimisation Techniques. In: Borgelt, C., Gil, M., Sousa, J., Verleysen, M. (eds) Towards Advanced Data Analysis by Combining Soft Computing and Statistics. Studies in Fuzziness and Soft Computing, vol 285. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30278-7_27
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DOI: https://doi.org/10.1007/978-3-642-30278-7_27
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