Decision Support Systems
Models and tools that help people make well-informed decisions in complex, uncertain settings.
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Discovering Unique, Low-Energy Transition States Using Evolutionary Molecular Memetic Computing
MMH Ellabaan, YS Ong, SD Handoko, CK Kwoh, H Man. Cited by 11
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Multi-objective Optimization
K Deb, K Deb. Cited by 1259
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Evolutionary bilevel optimization
A Sinha, P Malo, K Deb. Cited by 32
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A design automation framework for computational bioenergetics in biological networks
C Angione, J Costanza, G Carapezza, P Lió, G Nicosia. Cited by 8
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Pareto Optimality in Organelle Energy Metabolism Analysis
C Angione, G Carapezza, J Costanza, P Lió, G Nicosia. Cited by 14
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A multi-objective evolutionary approach for generator scheduling
D Li, S Das, A Pahwa, K Deb. Cited by 8
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Genetic Algorithm–Based Design and Development of Particle-Reinforced Silicone Rubber for Soft Tooling Process
AK Nandi, K Deb, S Datta. Cited by 16
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An Evolutionary Algorithm Based Approach to Design Optimization Using Evidence Theory
RK Srivastava, K Deb, R Tulshyan. Cited by 23
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Multi-agent multi-issue negotiations with incomplete information: A Genetic Algorithm based on discrete surrogate approach
AJ Kattan, YS Ong, E Galván. Cited by 9
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Multi objective design for bacterial communication networks
C Angione, G Carapezza, J Costanza, P Lió, G Nicosia. Cited by 1
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Solving clustering problems using bi-objective evolutionary optimisation and knee finding algorithms
G Recio, K Deb. Cited by 3
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Individual penalty based constraint handling using a hybrid bi-objective and penalty function approach
R Datta, K Deb. Cited by 13