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We consider the problem of constrained multi-objective (MO) blackbox optimization using expensive function evaluations, where the goal is to approximate the true Pareto set of solutions satisfying a set of constraints while minimizing the number of function evaluations.
Lipschitzian optimization without the lipschitz constant
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Evolutionary algorithms for multiobjective optimization: Methods and applications , volume 63
Eckart Zitzler · 1999
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Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
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Max-value entropy search for multi-objective Bayesian optimization
Syrine Belakaria, Aryan Deshwal, and Janardhan Doppa · 2019
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Predictive entropy search for multi-objective bayesian optimization with constraints
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Syrine Belakaria, Aryan Deshwal, Nitthilan Kannappan Jayakodi, and Janardhan Rao Doppa
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Machine learning enabled fast multi-objective optimization for electrified aviation power system design
Syrine Belakaria, Derek Jackson, Yue Cao, Janardhan Rao Doppa, and Xiaonan Lu
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Design of multi-output switched-capacitor voltage regulator via machine learning
Syrine Belakaria, Zhiyuan Zhou, Aryan Deshwal, Janardhan Rao Doppa, Partha Pande, and Deuk Heo
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A fast and elitist multiobjective genetic algorithm: Nsga-ii
Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan
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Optimizing discrete spaces via expensive evaluations: A learning to search framework
Aryan Deshwal, Syrine Belakaria, Janardhan Rao Doppa, and Alan Fern
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