A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise
Computational Optimization and Applications
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Searching for robust pareto-optimal solutions in multi-objective 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
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Pareto-dominance in noisy environments
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Architecture-based reliability evaluation under uncertainty
Proceedings of the joint ACM SIGSOFT conference -- QoSA and ACM SIGSOFT symposium -- ISARCS on Quality of software architectures -- QoSA and architecting critical systems -- ISARCS
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
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Real-world optimization problems are often subject to uncertainties, which can arise regarding stochastic model parameters, objective functions and decision variables. These uncertainties can take different forms in terms of distribution, bound and central tendency. In the multiobjective context, several studies have been proposed to take uncertainty into account, and most of them propose an extension of Pareto dominance to the stochastic case. In this paper, we pursue a slightly different approach where the optimization goal is defined in terms of a quality indicator, i.e., an objective function on the set of Pareto set approximations. We consider the scenario that each solution is inherently associated with a probability distribution over the objective space, without assuming a ’true’ objective vector per solution. We propose different algorithms which optimize the quality indicator, and preliminary simulation results indicate advantages over existing methods such as averaging, especially with many objective functions.