Abstract
Solving multi-objective problems usually results in a set of Perto-optimal solutions, or a Pareto front. Assessing the quality of these solutions, however, and comparing the performance of different multi-objective optimisers is still not very well understood. Current trends either model the outcome of the optimiser as a probability density function in the objective space, or defines an indicator that quantify the overall performance of the optimiser. Here an approach based on the concept of mutual information is proposed. The approach models the probability density function of the optimisers’ output and use that to define an indicator, namely the amount of shared information among the compared Pareto fronts. The strength of the new approach is not only in better assessment of performance but also the interpretability of the results it provides.
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Moubayed, N.A., Petrovski, A., McCall, J. (2013). Mutual Information for Performance Assessment of Multi Objective Optimisers: Preliminary Results. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_65
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DOI: https://doi.org/10.1007/978-3-642-41278-3_65
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