On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th 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
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
On the Integration of a TSP Heuristic into an EA for the Bi-objective Ring Star Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Introduction to Evolutionary Multiobjective Optimization
Multiobjective Optimization
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
An archived-based stochastic ranking evolutionary algorithm (asrea) for multi-objective optimization
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
On the computation of the empirical attainment function
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Bi-objective portfolio optimization using a customized hybrid NSGA-II procedure
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Advances in evolutionary multi-objective optimization
SSBSE'12 Proceedings of the 4th international conference on Search Based Software Engineering
The relationship between the covered fraction, completeness and hypervolume indicators
EA'11 Proceedings of the 10th international conference on Artificial Evolution
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The attainment function has been proposed as a measure of the statistical performance of stochastic multiobjective optimisers which encompasses both the quality of individual non-dominated solutions in objective space and their spread along the trade-off surface. It has also been related to results from random closed-set theory, and cast as a mean-like, first-order moment measure of the outcomes of multiobjective optimisers. In this work, the use of more informative, second-order moment measures for the evaluation and comparison of multiobjective optimiser performance is explored experimentally, with emphasis on the interpretability of the results.