Decision Support Systems
Models and tools that help people make well-informed decisions in complex, uncertain settings.
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Generative multiform Bayesian optimization
Z Guo, H Liu, YS Ong, X Qu, Y Zhang, J Zheng. Cited by 22
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Text revision by on-the-fly representation optimization
J Li, Z Li, T Ge, I King, MR Lyu. Cited by 23
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Intelligent financial forecasting with an improved chemical reaction optimization algorithm based dendritic neuron model
SC Nayak, S Dehuri, SB Cho. Cited by 24
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Toward interpretable-AI policies using evolutionary nonlinear decision trees for discrete-action systems
Y Dhebar, K Deb, S Nageshrao, L Zhu, D Filev. Cited by 25
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Scaling multiobjective evolution to large data with minions: A bayes-informed multitask approach
Z Chen, A Gupta, L Zhou, YS Ong. Cited by 25
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Multispace evolutionary search for large-scale optimization with applications to recommender systems
L Feng, Q Shang, Y Hou, KC Tan, YS Ong. Cited by 30
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Machine learning-based framework to cover optimal pareto-front in many-objective optimization
A Asilian Bidgoli, S Rahnamayan, B Erdem, Z Erdem, A Ibrahim, K Deb, et al. Cited by 34
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Scalable transfer evolutionary optimization: Coping with big task instances
M Shakeri, E Miahi, A Gupta, YS Ong. Cited by 33
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Benefits of sparse population sampling in multi-objective evolutionary computing for large-scale sparse optimization problems
I Kropp, AP Nejadhashemi, K Deb. Cited by 43
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A customized genetic algorithm for bi-objective routing in a dynamic network
A Maskooki, K Deb, M Kallio. Cited by 33
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A cell-based fast memetic algorithm for automated convolutional neural architecture design
J Dong, B Hou, L Feng, H Tang, KC Tan, YS Ong. Cited by 34
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Scheduling by NSGA-II: review and bibliometric analysis
I Rahimi, AH Gandomi, K Deb, F Chen, MR Nikoo. Cited by 109