Evolutionary algorithms for constrained engineering problems
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A Computationally Efficient Feasible Sequential Quadratic Programming Algorithm
SIAM Journal on Optimization
Mathematics of Computation
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
Robust design of multilayer optical coatings by means ofevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Local performance of the (1 + 1)-ES in a noisy environment
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Engineering Applications of Artificial Intelligence
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In many real-world design problems, uncertainties are often present and practically impossible to avoid. Many existing works on Evolutionary Algorithm (EA) for handling uncertainty have emphasized on introducing some prior structure of the uncertainty or noise to the variable domain and conducting sensitivity analysis based on the assumed information. In this paper, we present an evolutionary design optimization that handles the presence of uncertainty with respect to the desired robust performance in mind, which we call an inverse robust design. The scheme, unlike others developed to represent uncertainty does not assume any structure of the uncertainty involved; hence it is particularly useful when there is very little information about the uncertainties available. In our formulation, we model the clustering of uncertain events in families of nested sets using a multi-level optimization searches within the multi-objective evolutionary search. Empirical studies were conducted on synthetic functions to demonstrate that our algorithm converges to a set of designs with non-dominated nominal performances and robustness to the presence of uncertainties.