Modelling extremal events: for insurance and finance
Modelling extremal events: for insurance and finance
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Meta-Modeling in Multiobjective Optimization
Multiobjective Optimization
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Effective Hybrid Stochastic Local Search Algorithms for Biobjective Permutation Flowshop Scheduling
HM '09 Proceedings of the 6th International Workshop on Hybrid Metaheuristics
Proceedings of the 12th annual conference on Genetic and evolutionary computation
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
EA'09 Proceedings of the 9th international conference on Artificial evolution
Automatic configuration of multi-objective ACO algorithms
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Adaptive "Anytime" two-phase local search
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
Multi-objective evolutionary algorithms for feature selection: application in bankruptcy prediction
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems
Computers and Operations Research
On the computation of the empirical attainment function
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Improving the anytime behavior of two-phase local search
Annals of Mathematics and Artificial Intelligence
Computers and Operations Research
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
De Novo Design of Potential RecA Inhibitors Using MultiObjective Optimization
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
The relationship between the covered fraction, completeness and hypervolume indicators
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Optimising anti-spam filters with evolutionary algorithms
Expert Systems with Applications: An International Journal
Controllable procedural map generation via multiobjective evolution
Genetic Programming and Evolvable Machines
On the performance comparison of multi-objective evolutionary UAV path planners
Information Sciences: an International Journal
An approach to visualizing the 3D empirical attainment function
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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The performance of stochastic optimisers can be assessed experimentally on given problems by performing multiple optimisation runs, and analysing the results. Since an optimiser may be viewed as an estimator for the (Pareto) minimum of a (vector) function, stochastic optimiser performance is discussed in the light of the criteria applicable to more usual statistical estimators. Multiobjective optimisers are shown to deviate considerably from standard point estimators, and to require special statistical methodology. The attainment function is formulated, and related results from random closed-set theory are presented, which cast the attainment function as a mean-like measure for the outcomes of multiobjective optimisers. Finally, a covariance-measure is defined, which should bring additional insight into the stochastic behaviour of multiobjective optimisers. Computational issues and directions for further work are discussed at the end of the paper.