Decision Support Systems
Models and tools that help people make well-informed decisions in complex, uncertain settings.
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Probabilistic constraint handling in the framework of joint evolutionary-classical optimization with engineering applications
R Datta, MS Bittermann, K Deb, Ö Ciftcioglu. Cited by 11
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An adaptive normalization based constrained handling methodology with hybrid bi-objective and penalty function approach
R Datta, K Deb. Cited by 22
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Unconstrained scalable test problems for single-objective bilevel optimization
A Sinha, P Malo, K Deb. Cited by 23
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Approximating a multi-dimensional Pareto front for a land use management problem: A modified MOEA with an epigenetic silencing metaphor
O Chikumbo, ED Goodman, K Deb. Cited by 61
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Handling many-objective problems using an improved NSGA-II procedure
K Deb, H Jain. Cited by 101
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QuickVina: Accelerating AutoDock Vina Using Gradient-Based Heuristics for Global Optimization
SD Handoko, X Ouyang, CTT Su, CK Kwoh, YS Ong. Cited by 75
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Evolution by Adapting Surrogates
MN Le, YS Ong, S Menzel, Y Jin, B Sendhoff. Cited by 72
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An evolutionary based Bayesian design optimization approach under incomplete information
RK Srivastava, K Deb. Cited by 17
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A Conceptual Modeling of Meme Complexes in Stochastic Search
X Chen, YS Ong. Cited by 35
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A tree-structured covalent-bond-driven molecular memetic algorithm for optimization of ring-deficient molecules
MMH Ellabaan, SD Handoko, YS Ong, CK Kwoh, S Bahnassy, FM Elassawy, H Man. Cited by 10
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A genetic algorithm based augmented Lagrangian method for constrained optimization
K Deb, S Srivastava. Cited by 44
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Objective Reduction in Many-Objective Optimization: Linear and Nonlinear Algorithms
DK Saxena, JA Duro, A Tiwari, K Deb, Q Zhang. Cited by 305