Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Robust Software - No More Excuses
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Fitness inheritance for noisy evolutionary multi-objective optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Searching for robust pareto-optimal solutions in multi-objective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Efficient search for robust solutions by means of evolutionary algorithms and fitness approximation
IEEE Transactions on Evolutionary Computation
Evolutionary algorithms for optimization problems with uncertainties and hybrid indices
Information Sciences: an International Journal
Structural and Multidisciplinary Optimization
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Uncertainties are commonly present in optimization systems, and when they are considered in the design stage, the problem usually is called a robust optimization problem. Robust optimization problems can be treated as noisy optimization problems, as worst case minimization problems, or by considering the mean and standard deviation values of the objective and constraint functions. The worst case scenario is preferred when the effects of the uncertainties on the nominal solution are critical to the application under consideration. Based on this worst case scenario, we developed the [I]RMOEA (Interval Robust Multi-Objective Evolutionary Algorithm), a hybrid method that combines interval analysis techniques to deal with the uncertainties in a deterministic way and a multi-objective evolutionary algorithm. We introduce [I]RMOEA and illustrate it on three robust test functions based on the ZDT problems. The results show that [I]RMOEA is an adequate way of tackling robust optimization problems with evolutionary techniques taking advantage of the interval analysis framework.