Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Genetic Algorithms in Noisy Environments
Machine Learning
Evolutionary Multi-objective Ranking with Uncertainty and Noise
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Trade-off between performance and robustness: an evolutionary multiobjective approach
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
Stochastic ranking for constrained evolutionary optimization
IEEE Transactions on Evolutionary Computation
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
An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization
IEEE Transactions on Evolutionary Computation
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This paper sets forth a new approach to robust evolutionary computing. In particular, the proposed approach allows users to specify the probability of success in meeting design specifications in the presence of uncertainties. Three benchmark problems have been considered to demonstrate the proposed approach. In addition, a robust electromagnet design example is also considered. The results illustrate quantitative correspondence between the prescribed and the computed robustness.