Computational Optimization and Applications
A pareto following variation operator for fast-converging multiobjective evolutionary algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Memetic Algorithms for Feature Selection on Microarray Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
New Approaches to Coevolutionary Worst-Case Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
Evolutionary algorithms for minimax problems in robust design
IEEE Transactions on Evolutionary Computation
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
An investigation on noise-induced features in robust evolutionary multi-objective optimization
Expert Systems with Applications: An International Journal
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Finding multiple first order saddle points using a valley adaptive clearing genetic algorithm
CIRA'09 Proceedings of the 8th IEEE international conference on Computational intelligence in robotics and automation
Evolutionary regression modeling with active learning: an application to rainfall runoff modeling
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Research frontier: memetic computation-past, present & future
IEEE Computational Intelligence Magazine
Memetic compact differential evolution for cartesian robot control
IEEE Computational Intelligence Magazine
Generalizing surrogate-assisted evolutionary computation
IEEE Transactions on Evolutionary Computation
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
An archive maintenance scheme for finding robust solutions
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Particle swarm optimization with composite particles in dynamic environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Noise analysis compact genetic algorithm
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
A new fitness estimation strategy for particle swarm optimization
Information Sciences: an International Journal
Application of variational granularity language sets in interactive genetic algorithms
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
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Solving design optimization problems using evolutionary algorithms has always been perceived as finding the optimal solution over the entire search space. However, the global optima may not always be the most desirable solution in many real-world engineering design problems. In practice, if the global optimal solution is very sensitive to uncertainties, for example, small changes in design variables or operating conditions, then it may not be appropriate to use this highly sensitive solution. In this paper, we focus on combining evolutionary algorithms with function approximation techniques for robust design. In particular, we investigate the application of robust genetic algorithms to problems with high dimensions. Subsequently, we present a novel evolutionary algorithm based on the combination of a max-min optimization strategy with a Baldwinian trust-region framework employing local surrogate models for reducing the computational cost associated with robust design problems. Empirical results are presented for synthetic test functions and aerodynamic shape design problems to demonstrate that the proposed algorithm converges to robust optimum designs on a limited computational budget