Predicting Effective Control Parameters for Differential Evolution using Cluster Analysis of Objective Function Features
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A methodology is introduced which uses three simple objective function features to predict effective control parameters for differential evolution. This is achieved using cluster analysis techniques to classify objective functions using these features. Information on prior performance of various control parameters for each classification is then used to determine which control parameters to use in future optimisations. Our approach is compared to an state-of-the-art adaptive technique along with non-adaptive techniques. Two accepted bench mark suites are used to compare performance, in all cases we show that the improvement resulting from our approach is statistically significant. The majority of the computational effort of this methodology is performed off-line, however even taking into account the additional on-line cost our approach outperforms other adaptive techniques. We also study the key tuning parameters of our methodology, which further support the finding that the simple features selected are predictors of effective control parameters. The findings presented in this paper are significant because they show that simple to calculate features of objective functions can help select control parameters for optimisation algorithms. This can have an immediate positive impact the application of these optimisation algorithms on real world problems where it is often difficult to select effective control parameters.
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