Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
On the computation of the empirical attainment function
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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This paper investigates the relationship between the covered fraction, completeness, and (weighted) hypervolume indicators for assessing the quality of the Pareto-front approximations produced by multiobjective optimizers. It is shown that these unary quality indicators are all, by definition, weighted Hausdorff measures of the intersection of the region attained by such an optimizer outcome in objective space with some reference set. Moreover, when the optimizer is stochastic, the indicators considered lead to real-valued random variables following particular probability distributions. Expressions for the expected value of these distributions are derived, and shown to be directly related to the first-order attainment function.