Advances in the Dempster-Shafer theory of evidence
Advances in the Dempster-Shafer theory of evidence
Noisy Local Optimization with Evolution Strategies
Noisy Local Optimization with Evolution Strategies
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Pareto-Front Exploration with Uncertain Objectives
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multi-scale data fusion using Dempster-Shafer evidence theory
Integrated Computer-Aided Engineering
International Journal of Approximate Reasoning
On Uncertainty and Robustness in Evolutionary Optimization-Based MCDM
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
A review of possibilistic approaches to reliability analysis and optimization in engineering design
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: applications and services
Handling uncertainties in evolutionary multi-objective optimization
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
GFS-based analysis of vague databases in high performance athletics
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
Computers and Electronics in Agriculture
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Hierarchical stochastic metamodels based on moving least squares and polynomial chaos expansion
Structural and Multidisciplinary Optimization
Multi-objective reliability-based optimization with stochastic metamodels
Evolutionary Computation
Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Evolutionary Computation
Information Sciences: an International Journal
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Multi-objective evolutionary algorithms (MOEAs) have proven to be a powerful tool for global optimization purposes of deterministic problem functions. Yet, in many real-world problems, uncertainty about the correctness of the system model and environmental factors does not allow to determine clear objective values. Stochastic sampling as applied in noisy EAs neglects that this so-called epistemic uncertainty is not an inherent property of the system and cannot be reduced by sampling methods. Therefore, some extensions for MOEAs to handle epistemic uncertainty in objective functions are proposed. The extensions are generic and applicable to most common MOEAs. A density measure for uncertain objectives is proposed to maintain diversity in the nondominated set. The approach is demonstrated to the reliability optimization problem, where uncertain component failure rates are usual and exhaustive tests are often not possible due to time and budget reasons.