Decision Support Systems
Models and tools that help people make well-informed decisions in complex, uncertain settings.
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Hierarchical surrogate-assisted evolutionary optimization framework
Z Zhou, YS Ong, PB Nair. Cited by 26
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Design and implementation of an efficient multi-cluster GridRPC system
QT Ho, W Cai, YS Ong. Cited by 4
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A Multi-cluster Grid Enabled Evolution Framework for Aerodynamic Airfoil Design Optimization
HK Ng, D Lim, YS Ong, B Lee, L Freund, S Parvez, B Sendhoff. Cited by 14
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Grid Enabled Optimization
HK Ng, YS Ong, T Hung, B Lee. Cited by 8
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Surrogate-Assisted Evolutionary Optimization Frameworks for High-Fidelity Engineering Design Problems
YS Ong, PB Nair, AJ Keane, KW Wong. Cited by 150
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A Tutorial on Evolutionary Multi-Objective Optimization (EMO)
K Deb
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On Properly Pareto Optimal Solutions
PK Shukla, J Dutta, K Deb. Cited by 1
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04461 Summary – Practical Approaches to Multi-Criterion Optimization
J Branke, K Deb, K Miettinen, RE Steuer. Cited by 3
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Design of optimum cross-sections for load-carrying members using multi-objective evolutionary algorithms
D Datta, K Deb. Cited by 10
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I-EMO: An Interactive Evolutionary Multi-objective Optimization Tool
K Deb, S Chaudhuri. Cited by 34
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Comparing Classical Generating Methods with an Evolutionary Multi-objective Optimization Method
PK Shukla, K Deb, S Tiwari. Cited by 47
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Practical Approaches to Multi-Objective Optimization
J Branke, K Deb, K Miettinen, RE Steuer. Cited by 41