Healthcare II: multi-objective simulation optimization for a cancer treatment center
Proceedings of the 33nd conference on Winter simulation
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
PPSN VII Proceedings of the 7th International Conference 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
Variable-sample methods for stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Minimum spanning trees made easier via multi-objective optimization
Natural Computing: an international journal
Convergence of stochastic search algorithms to gap-free pareto front approximations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Benefits and drawbacks for the use of epsilon-dominance in evolutionary multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A provably convergent heuristic for stochastic bicriteria integer programming
Journal of Heuristics
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
A survey on metaheuristics for stochastic combinatorial optimization
Natural Computing: an international journal
A multiobjective metaheuristic for a mean-risk multistage capacity investment problem
Journal of Heuristics
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Computers and Operations Research
A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem
Computational Optimization and Applications
Two metaheuristics for multiobjective stochastic combinatorial optimization
SAGA'05 Proceedings of the Third international conference on StochasticAlgorithms: foundations and applications
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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For stochastic multi-objective combinatorial optimization (SMOCO) problems, the adaptive Pareto sampling (APS) framework has been proposed, which is based on sampling and on the solution of deterministic multi-objective subproblems. We show that when plugging in the well-known simple evolutionary multi-objective optimizer (SEMO) as a subprocedure into APS, ε-dominance has to be used to achieve fast convergence to the Pareto front. Two general theorems are presented indicating how runtime complexity results for APS can be derived from corresponding results for SEMO. This may be a starting point for the runtime analysis of evolutionary SMOCO algorithms.