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Many scientific and industrial applications require the joint optimization of multiple, potentially competing objectives.
Fonctions de Répartition à n Dimensions et Leurs Marges
Sklar, A · 1959
Earlier work this paper cites.
On bayesian methods for seeking the extremum
Močkus, J · 1975
Earlier work this paper cites.
Multivariate Models and Dependence Concepts
Joe, H · 1997
Earlier work this paper cites.
Information theory and an extension of the maximum likelihood principle
Akaike, H · 1998
Earlier work this paper cites.
Efficient global optimization of expensive black-box functions
Jones, D. R., Schonlau, M., and Welch, W. J · 1998
Earlier work this paper cites.
Vines – A New Graphical Model for Dependent Random Variables
Bedford, T. and Cooke, R. M · 2002
Earlier work this paper cites.
Using copulae to bound the value-at-risk for functions of dependent risks
Embrechts, P., Höing, A., and Juri, A · 2003
Earlier work this paper cites.
Performance assessment of multiobjective optimizers: An analysis and review
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., and Da Fonseca, V. G · 2003
Earlier work this paper cites.
Survey of multi-objective optimization methods for engineering
Marler, R. T. and Arora, J. S · 2004
Earlier work this paper cites.
Searching for robust pareto-optimal solutions in multi-objective optimization
Deb, K. and Gupta, H · 2005
Earlier work this paper cites.
Single-and multi-objective evolutionary design optimization assisted by gaussian random field metamodels
Emmerich, M · 2005
Earlier work this paper cites.
A tutorial on the performance assessment of stochastic multiobjective optimizers
Fonseca, C. M., Knowles, J. D., Thiele, L., Zitzler, E., et al · 2005
Earlier work this paper cites.
Caco-2 cell permeability assays to measure drug absorption
Van Breemen, R. B. and Li, Y · 2005
Earlier work this paper cites.
An improved dimension-sweep algorithm for the hypervolume indicator
Fonseca, C. M., Paquete, L., and López-Ibánez, M · 2006
Earlier work this paper cites.
The cma evolution strategy: a comparing review
Hansen, N · 2006
Earlier work this paper cites.
Parego: A hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems
Knowles, J · 2006
Earlier work this paper cites.
Ranking-dominance and many-objective optimization
Kukkonen, S. and Lampinen, J · 2007
Earlier work this paper cites.
An introduction to copulas
Nelsen, R. B · 2007
Earlier work this paper cites.
Pareto-based multiobjective machine learning: An overview and case studies
Jin, Y. and Sendhoff, B · 2008
Earlier work this paper cites.
Pair-copula constructions of multiple dependence
Aas, K., Czado, C., Frigessi, A., and Bakken, H · 2009
Earlier work this paper cites.
A sequential design for approximating the pareto front using the expected pareto improvement function
Bautista, D. C · 2009
Earlier work this paper cites.
Reliability-based optimization using evolutionary algorithms
Deb, K., Gupta, S., Daum, D., Branke, J., Mall, A. K., and Padmanabhan, D · 2009
Earlier work this paper cites.
An informational approach to the global optimization of expensive-to-evaluate functions
Villemonteix, J., Vazquez, E., and Walter, E · 2009
Earlier work this paper cites.
Tail dependence functions and vine copulas
Joe, H., Li, H., and Nikoloulopoulos, A. K · 2010
Earlier work this paper cites.
Hypervolume-based expected improvement: Monotonicity properties and exact computation
Emmerich, M. T., Deutz, A. H., and Klinkenberg, J. W · 2011
Earlier work this paper cites.
A many-objective test problem for visually examining diversity maintenance behavior in a decision space
Ishibuchi, H., Akedo, N., and Nojima, Y · 2011
Earlier work this paper cites.
Joint placement of phasor and power flow measurements for observability of power systems
Kavasseri, R. and Srinivasan, S. K · 2011
Earlier work this paper cites.
Computer experiments: Multiobjective optimization and sensitivity analysis
Svenson, J · 2011
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Multi-parameter optimization: identifying high quality compounds with a balance of properties
D. Segall, M · 2012
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Entropy search for information-efficient global optimization
Hennig, P. and Schuler, C. J · 2012
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Navigating the protein fitness landscape with gaussian processes
Romero, P. A., Krause, A., and Arnold, F. H · 2013
Cited alongside, same era.
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Gardner, J. R., Kusner, M. J., Xu, Z. E., Weinberger, K. Q., and Cunningham, J. P · 2014
Cited alongside, same era.
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Binois, M., Rullière, D., and Roustant, O · 2015
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Balandat, M., Karrer, B., Jiang, D. R., Daulton, S., Letham, B., Wilson, A. G., and Bakshy, E · 2020
Later among the works it cites.
The kalai-smorodinsky solution for many-objective bayesian optimization
Binois, M., Picheny, V., Taillandier, P., and Habbal, A · 2020
Later among the works it cites.
Fast differentiable sorting and ranking
Blondel, M., Teboul, O., Berthet, Q., and Djolonga, J · 2020
Later among the works it cites.
Differentiable expected hypervolume improvement for parallel multi-objective bayesian optimization
Daulton, S., Balandat, M., and Bakshy, E · 2020
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A repository of real-world datasets for data-driven evolutionary multiobjective optimization
He, C., Tian, Y., Wang, H., and Jin, Y · 2020
Later among the works it cites.
On-the-fly closed-loop materials discovery via bayesian active learning
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Parallel predictive entropy search for batch global optimization of expensive objective functions
Shah, A. and Ghahramani, Z · 2015
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Taking the human out of the loop: A review of bayesian optimization
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., and De Freitas, N · 2015
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Calandra, R., Seyfarth, A., Peters, J., and Deisenroth, M. P · 2016
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Haugh, M · 2016
Cited alongside, same era.
Necessary and sufficient conditions for surrogate functions of pareto frontiers and their synthesis using gaussian processes
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Kusne, A. G., Yu, H., Wu, C., Zhang, H., Hattrick-Simpers, J., DeCost, B., Sarker, S., Oses, C., Toher, C., Curtarolo, S., et al · 2020
Later among the works it cites.
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Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement
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Scalable bayesian optimization accelerates process optimization of penicillin production
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Bayesian optimization over discrete and mixed spaces via probabilistic reparameterization
Daulton, S., Wan, X., Eriksson, D., Balandat, M., Osborne, M. A., and Bakshy, E · 2022
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Joint entropy search for maximally-informed bayesian optimization
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