Real-World Applications of Multiobjective Optimization
Multiobjective Optimization
Interval robust multi-objective evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Reliability-based optimization using evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multicriteria decision making (MCDM): a framework for research and applications
IEEE Computational Intelligence Magazine
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
On the performance of metamodel assisted MOEA/D
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
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
Expensive multiobjective optimization by MOEA/D with Gaussian process model
IEEE Transactions on Evolutionary Computation
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
Exploiting overlap when searching for robust optima
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An archive maintenance scheme for finding robust solutions
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
An evolutionary computing approach to robust design in the presence of uncertainties
IEEE Transactions on Evolutionary Computation
Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion
Structural and Multidisciplinary Optimization
A novel probabilistic encoding for EAs applied to biclustering of microarray data
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Using the uncertainty handling CMA-ES for finding robust optima
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
Resampling methods for meta-model validation with recommendations for evolutionary computation
Evolutionary Computation
Hoeffding bound based evolutionary algorithm for symbolic regression
Engineering Applications of Artificial Intelligence
Robust solutions for the software project scheduling problem: a preliminary analysis
International Journal of Metaheuristics
An evolutionary linear programming algorithm for solving the stock reduction problem
International Journal of Computer Applications in Technology
Information Sciences: an International Journal
Finding robust solutions to dynamic optimization problems
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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For many real-world optimization problems, the robustness of a solution is of great importance in addition to the solution's quality. By robustness, we mean that small deviations from the original design, e.g., due to manufacturing tolerances, should be tolerated without a severe loss of quality. One way to achieve that goal is to evaluate each solution under a number of different scenarios and use the average solution quality as fitness. However, this approach is often impractical, because the cost for evaluating each individual several times is unacceptable. In this paper, we present a new and efficient approach to estimating a solution's expected quality and variance. We propose to construct local approximate models of the fitness function and then use these approximate models to estimate expected fitness and variance. Based on a variety of test functions, we demonstrate empirically that our approach significantly outperforms the implicit averaging approach, as well as the explicit averaging approaches using existing estimation techniques reported in the literature