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In contrast to single-objective optimization (SOO), multi-objective optimization (MOO) requires an optimizer to find the Pareto frontier, a subset of feasible solutions that are not dominated by other feasible solutions.
Sample-efficient neural architecture search by learning action space
Linnan Wang, Saining Xie, Teng Li, Rodrigo Fonseca, and Yuandong Tian · 1906
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Once for all: Train one network and specialize it for efficient deployment
Han Cai, Chuang Gan, Tianzhe Wang, Zhekai Zhang, and Song Han · 1908
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On the likelihood that one unknown probability exceeds another in view of the evidence of two samples
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On finding the maxima of a set of vectors
H. T. Kung, F. Luccio, and F. P. Preparata · 1975
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A convergent interactive cutting-plane algorithm for multiobjective optimization
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Muiltiobjective optimization using nondominated sorting in genetic algorithms
Nidamarthi Srinivas and Kalyanmoy Deb · 1994
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Efficient global optimization of expensive black-box functions
Donald R Jones, Matthias Schonlau, and William J Welch · 1998
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Evolutionary computation and convergence to a pareto front
David A Van Veldhuizen and Gary B Lamont · 1998
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Comparison of multiobjective evolutionary algorithms: Empirical results
Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele · 2000
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Scalable multi-objective optimization test problems
K. Deb, L. Thiele, M. Laumanns, and E. Zitzler · 2002
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The balance between proximity and diversity in multiobjective evolutionary algorithms
Peter AN Bosman and Dirk Thierens · 2003
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The measure of pareto optima applications to multi-objective metaheuristics
M. Fleischer · 2003
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Performance assessment of multiobjective optimizers: an analysis and review
E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, and V.G. da Fonseca · 2003
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Multiobjective gas, quantitative indices, and pattern classification
Sanghamitra Bandyopadhyay, Sankar K Pal, and B Aruna · 2004
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Parego: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
J. Knowles · 2005
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Faster s-metric calculation by considering dominated hypervolume as klee’s measure problem, 2006
Nicola Beume and Günter Rudolph · 2006
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Sms-emoa: Multiobjective selection based on dominated hypervolume
Nicola Beume, Boris Naujoks, and Michael Emmerich · 2006
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Bandit based monte-carlo planning
Levente Kocsis and Csaba Szepesvári · 2006
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Research in the performance assessment of multi-objective optimization evolutionary algorithms
Guoqiang Deng, Zhangcan Huang, and Min Tang · 2007
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Covariance matrix adaptation for multi-objective optimization
Christian Igel, Nikolaus Hansen, and Stefan Roth · 2007
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Moea/d: A multiobjective evolutionary algorithm based on decomposition
Qingfu Zhang and Hui Li · 2007
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Multiobjective optimization for crash safety design of vehicles using stepwise regression model
Xingtao Liao, Qing Li, Xujing Yang, Weigang Zhang, and Wei Li · 2008
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Multiobjective optimization on a limited budget of evaluations using model-assisted-metric selection
Wolfgang Ponweiser, Tobias Wagner, Dirk Biermann, and Markus Vincze · 2008
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On the complexity of computing the hypervolume indicator
Nicola Beume, Carlos M. Fonseca, Manuel Lopez-Ibanez, LuÍs Paquete, and Jan Vahrenhold · 2009
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Learning multiple layers of features from tiny images
Parallel architecture and hyperparameter search via successive halving and classification
Manoj Kumar, George E. Dahl, Vijay Vasudevan, and Mohammad Norouzi · 2018
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A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization
Linqiang Pan, Cheng He, Ye Tian, Handing Wang, Xingyi Zhang, and Yaochu Jin · 2018
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A flexible multi-objective bayesian optimization approach using random scalarizations
Biswajit Paria, Kirthevasan Kandasamy, and Barnabás Póczos · 2018
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Multi-objective bayesian global optimization using expected hypervolume improvement gradient
Kaifeng Yang, Michael Emmerich, André Deutz, and Thomas Bäck · 2018
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Max-value entropy search for multi-objective bayesian optimization
Syrine Belakaria, Aryan Deshwal, and Janardhan Rao Doppa · 2019
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Alex Krizhevsky · 2009
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A mono surrogate for multiobjective optimization
Ilya Loshchilov, Marc Schoenauer, and Michèle Sebag · 2010
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Optimistic optimization of a deterministic function without the knowledge of its smoothness
Rémi Munos · 2011
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Pareto rank learning in multi-objective evolutionary algorithms
Chun-Wei Seah, Yew-Soon Ong, Ivor W. Tsang, and Siwei Jiang · 2012
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An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: Solving problems with box constraints
Kalyanmoy Deb and Himanshu Jain · 2013
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Fast calculation of multiobjective probability of improvement and expected improvement criteria for pareto optimization
Ivo Couckuyt, Dirk Deschrijver, and Tom Dhaene · 2014
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Consistencies and contradictions of performance metrics in multiobjective optimization
Siwei Jiang, Yew-Soon Ong, Jie Zhang, and Liang Feng · 2014
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ProxylessNAS: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2019
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Applications of machine learning in drug discovery and development
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
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A cutting-plane method to nonsmooth multiobjective optimization problems
Douglas AG Vieira and Adriano Chaves Lisboa · 2019
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Evaluating the search phase of neural architecture search
Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann · 2019
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Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization
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Nas-bench-201: Extending the scope of reproducible neural architecture search
Xuanyi Dong and Yi Yang · 2020
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Monte carlo tree search in continuous spaces using voronoi optimistic optimization with regret bounds
Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Kaelbling, and Tomás Lozano-Pérez · 2020
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Nsga-ii with simple modification works well on a wide variety of many-objective problems
Lie Meng Pang, Hisao Ishibuchi, and Ke Shang · 2020
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Learning search space partition for black-box optimization using monte carlo tree search
Linnan Wang, Rodrigo Fonseca, and Yuandong Tian · 2020
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A survey of multiobjective evolutionary algorithms based on decomposition: Variants, challenges and future directions
Qian Xu, Zhanqi Xu, and Tao Ma · 2020
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Learning space partitions for path planning
Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, and Yuandong Tian · 2021
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Derivative free optimization via repeated classification
Tatsunori Hashimoto, Steve Yadlowsky, and John Duchi · 2036
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Tatsunori Hashimoto, Steve Yadlowsky, and John Duchi · 2036
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