Bibliografías temáticas / Blackbox optimization

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Literatura académica sobre el tema "Blackbox optimization"

Autor: Grafiati
Publicado: 1 de junio de 2024

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Artículos de revistas sobre el tema "Blackbox optimization"

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Audet, Charles, Sébastien Le Digabel, and Mathilde Peyrega. "Linear equalities in blackbox optimization." Computational Optimization and Applications 61, no. 1 (2014): 1–23. http://dx.doi.org/10.1007/s10589-014-9708-2.

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Audet, Charles, J. E. Dennis, and Sébastien Le Digabel. "Trade-off studies in blackbox optimization." Optimization Methods and Software 27, no. 4-5 (2012): 613–24. http://dx.doi.org/10.1080/10556788.2011.571687.

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Audet, Charles, Alain Batailly, and Solène Kojtych. "Escaping Unknown Discontinuous Regions in Blackbox Optimization." SIAM Journal on Optimization 32, no. 3 (2022): 1843–70. http://dx.doi.org/10.1137/21m1420915.

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Gramacy, Robert B., Genetha A. Gray, Sébastien Le Digabel, et al. "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization." Technometrics 58, no. 1 (2016): 1–11. http://dx.doi.org/10.1080/00401706.2015.1014065.

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Chen, Hao, and William J. Welch. "Comment: Expected Improvement for Efficient Blackbox Constrained Optimization." Technometrics 58, no. 1 (2016): 12–15. http://dx.doi.org/10.1080/00401706.2015.1044119.

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Audet, Charles, Gilles Caporossi, and Stéphane Jacquet. "Binary, unrelaxable and hidden constraints in blackbox optimization." Operations Research Letters 48, no. 4 (2020): 467–71. http://dx.doi.org/10.1016/j.orl.2020.05.011.

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Audet, Charles, Jean Bigeon, Romain Couderc, and Michael Kokkolaras. "Sequential stochastic blackbox optimization with zeroth-order gradient estimators." AIMS Mathematics 8, no. 11 (2023): 25922–56. http://dx.doi.org/10.3934/math.20231321.

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<abstract><p>This work considers stochastic optimization problems in which the objective function values can only be computed by a blackbox corrupted by some random noise following an unknown distribution. The proposed method is based on sequential stochastic optimization (SSO), i.e., the original problem is decomposed into a sequence of subproblems. Each subproblem is solved by using a zeroth-order version of a sign stochastic gradient descent with momentum algorithm (i.e., ZO-signum) and with increasingly fine precision. This decomposition allows a good exploration of the space w
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Audet, Charles, and Michael Kokkolaras. "Blackbox and derivative-free optimization: theory, algorithms and applications." Optimization and Engineering 17, no. 1 (2016): 1–2. http://dx.doi.org/10.1007/s11081-016-9307-4.

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Herraz, Mahfoud, Jean-Max Redonnet, Mohammed Sbihi, and Marcel Mongeau. "Blackbox optimization and surrogate models for machining free-form surfaces." Computers & Industrial Engineering 177 (March 2023): 109029. http://dx.doi.org/10.1016/j.cie.2023.109029.

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Sankaran, Anush, Olivier Mastropietro, Ehsan Saboori, et al. "Deeplite NeutrinoTM: A BlackBox Framework for Constrained Deep Learning Model Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (2021): 15166–74. http://dx.doi.org/10.1609/aaai.v35i17.17780.

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Designing deep learning-based solutions is becoming a race for training deeper models with a greater number of layers. While a large-size deeper model could provide competitive accuracy, it creates a lot of logistical challenges and unreasonable resource requirements during development and deployment. This has been one of the key reasons for deep learning models not being excessively used in various production environments, especially in edge devices. There is an immediate requirement for optimizing and compressing these deep learning models, to enable on-device intelligence. In this research,
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Tesis sobre el tema "Blackbox optimization"

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Dahito, Marie-Ange. "Constrained mixed-variable blackbox optimization with applications in the automotive industry." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAS017.

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Bon nombre de problèmes d'optimisation rencontrés dans l'industrie font appel à des systèmes complexes et n'ont pas de formulation analytique explicite : ce sont des problèmes d'optimisation de type boîte noire (ou blackbox en anglais). Ils peuvent être dits “mixtes”, auquel cas ils impliquent des variables de différentes natures (continues et discrètes), et avoir de nombreuses contraintes à satisfaire. De plus, les évaluations de l'objectif et des contraintes peuvent être numériquement coûteuses.Dans cette thèse, nous étudions des méthodes de résolution de tels problèmes complexes, à savoir d
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Anil, Gautham. "A Fitness Function Elimination Theory for Blackbox Optimization and Problem Class Learning." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5106.

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The modern view of optimization is that optimization algorithms are not designed in a vacuum, but can make use of information regarding the broad class of objective functions from which a problem instance is drawn. Using this knowledge, we want to design optimization algorithms that execute quickly (efficiency), solve the objective function with minimal samples (performance), and are applicable over a wide range of problems (abstraction). However, we present a new theory for blackbox optimization from which, we conclude that of these three desired characteristics, only two can be maximized by
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Bittar, Thomas. "Stochastic optimization of maintenance scheduling : blackbox methods, decomposition approaches - Theoretical and numerical aspects." Thesis, Marne-la-vallée, ENPC, 2021. http://www.theses.fr/2021ENPC2004.

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Le but de cette thèse est de développer des algorithmes pour la planification optimale de la maintenance. On s'intéresse à des systèmes de grande taille constitués de plusieurs composants liés par un stock commun de pièces de rechange. Les tests numériques sont effectués sur des systèmes de composants d'une même centrale hydroélectrique. La première partie est consacrée à l'étude des méthodes de type boîte noire qui sont souvent utilisées pour la planification de la maintenance. On s'intéresse à un algorithme basé sur le krigeage, Efficient Global Optimization (EGO), et à une méthode de recher
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Hemker, Thomas. "Derivative free surrogate optimization for mixed integer nonlinear black box problems in engineering." Düsseldorf VDI-Verl, 2009. http://d-nb.info/995156654/04.

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Draheim, Patrick [Verfasser], Gabriel [Akademischer Betreuer] Zachmann, Gabriel [Gutachter] Zachmann, and Marc-Erich [Gutachter] Latoschik. "New Concepts for Virtual Testbeds : Data Mining Algorithms for Blackbox Optimization based on Wait-Free Concurrency and Generative Simulation / Patrick Draheim ; Gutachter: Gabriel Zachmann, Marc-Erich Latoschik ; Betreuer: Gabriel Zachmann." Bremen : Staats- und Universitätsbibliothek Bremen, 2018. http://d-nb.info/1176103636/34.

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Atamna, Asma. "Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS010/document.

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On s'intéresse à l'étude d'algorithmes stochastiques pour l'optimisation numérique boîte-noire. Dans la première partie de cette thèse, on présente une méthodologie pour évaluer efficacement des stratégies d'adaptation du step-size dans le cas de l'optimisation boîte-noire sans contraintes. Le step-size est un paramètre important dans les algorithmes évolutionnaires tels que les stratégies d'évolution; il contrôle la diversité de la population et, de ce fait, joue un rôle déterminant dans la convergence de l'algorithme. On présente aussi les résultats empiriques de la comparaison de trois méth
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Wang, Pei-Qi, and 王姵淇. "Apply Ant Colony Optimization to Test Case Prioritization for Blackbox Testing." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/5uz6rz.

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碩士<br>中原大學<br>工業與系統工程研究所<br>99<br>A mature software products have to pass two testing processes before being released, white-box testing and black-box testing, as much as possible to implement testing in different scenarios, to identify potential faults and defects. In order to improve software quality, testing engineers should perform regression testing for each software release. However, regression testing is a time consuming and expensive testing procedure, required to execute numerous test cases. Due to time, cost and labor constraints, how to effectively perform regression testing has be
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Libros sobre el tema "Blackbox optimization"

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Audet, Charles, and Warren Hare. Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5.

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Audet, Charles, and Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2018.

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Audet, Charles, and Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2017.

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Capítulos de libros sobre el tema "Blackbox optimization"

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Audet, Charles. "Blackbox Optimization." In Encyclopedia of Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-54621-2_723-1.

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Audet, Charles, and Warren Hare. "Biobjective Optimization." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_14.

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Audet, Charles, and Warren Hare. "Optimization Using Surrogates and Models." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_13.

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Audet, Charles, and Warren Hare. "Positive Bases and Nonsmooth Optimization." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_6.

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Audet, Charles, and Warren Hare. "Introduction: Tools and Challenges in Derivative-Free and Blackbox Optimization." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_1.

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Audet, Charles, and Warren Hare. "Model-Based Descent." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_10.

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Audet, Charles, and Warren Hare. "Model-Based Trust Region." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_11.

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Audet, Charles, and Warren Hare. "Variables and Constraints." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_12.

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Audet, Charles, and Warren Hare. "Mathematical Background." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_2.

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Audet, Charles, and Warren Hare. "The Beginnings of DFO Algorithms." In Derivative-Free and Blackbox Optimization. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_3.

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Actas de conferencias sobre el tema "Blackbox optimization"

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Hawkins, Byron, Brian Demsky, and Michael B. Taylor. "BlackBox: lightweight security monitoring for COTS binaries." In CGO '16: 14th Annual IEEE/ACM International Symposium on Code Generation and Optimization. ACM, 2016. http://dx.doi.org/10.1145/2854038.2854062.

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Dev, Rahul, Krishanu Kundu, Amrita Rai, Shiv Narain Gupta, Abhishek Kaushik, and Reshu Agarwal. "Design and Implementation of Blackbox in Vehicles." In 2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2024. http://dx.doi.org/10.1109/icrito61523.2024.10522349.

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Kurhe, Vaibhav Kiran, Pratik Karia, Shubhani Gupta, Abhishek Rose, and Sorav Bansal. "Automatic Generation of Debug Headers through BlackBox Equivalence Checking." In 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022. http://dx.doi.org/10.1109/cgo53902.2022.9741273.

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"Managing Computationally Expensive Blackbox Multiobjective Optimization Problems with Libensemble." In 2020 Spring Simulation Conference. Society for Modeling and Simulation International (SCS), 2020. http://dx.doi.org/10.22360/springsim.2020.hpc.001.

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Hutter, Frank, Holger Hoos, and Kevin Leyton-Brown. "An evaluation of sequential model-based optimization for expensive blackbox functions." In Proceeding of the fifteenth annual conference companion. ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2501592.

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Audet, Charles, Sébastien Le Digabel, Ludovic Salomon, and Christophe Tribes. "Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite." In GECCO '22: Genetic and Evolutionary Computation Conference. ACM, 2022. http://dx.doi.org/10.1145/3520304.3534019.

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Tehrani, H. Mazaheri, A. Frances, R. Asensi, and J. Uceda. "Blackbox Equivalent Switching Model Identification of DC-DC Power Electronic Converters Using optimization Algorithms." In 2021 IEEE Fourth International Conference on DC Microgrids (ICDCM). IEEE, 2021. http://dx.doi.org/10.1109/icdcm50975.2021.9504611.

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Hartpence, Bruce, and Andres Kwasinski. "Considering the Blackbox: An Investigation of Optimization Techniques with Completely Balanced Datasets of Packet Traffic." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006508.

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Chamseddine, Ibrahim M., and Michael Kokkolaras. "Bio-Inspired Heuristic for Decoupling Network Configuration in Air Transportation System-of-Systems Design Optimization." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59424.

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Previous work in air transportation system-of-systems design optimization considered integrated aircraft sizing, fleet allocation and route network configuration. The associated nested multidisciplinary formulation posed a numerically challenging optimization problem; therefore, direct search methods with convergence properties were used to solve it. However, the complexity of the blackbox is such that it impedes greatly the solution of larger-scale problems, where the number of considered nodes in the route network is high. The research presented here adopts a rule-based route network design
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Wang, Lv, Teng Long, Lei Peng, and Li Liu. "Optimized Radial Basis Function Metamodel for Expensive Engineering Design Optimization." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87489.

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At the aim of alleviating the computational burden of complicated engineering optimization problems, metamodels have been widely employed to approximate the expensive blackbox functions. Among the popular metamodeling methods RBF metamodel well balances the global approximation accuracy, computational cost and implementation difficulty. However, the approximation accuracy of RBF metamodel is heavily influenced by the width factors of kernel functions, which are hard to determine and actually depend on the numerical behavior of expensive functions and distribution of samples. The main contribut
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