Letteratura scientifica selezionata sul tema "Blackbox optimization"
Cita una fonte nei formati APA, MLA, Chicago, Harvard e in molti altri stili
Consulta la lista di attuali articoli, libri, tesi, atti di convegni e altre fonti scientifiche attinenti al tema "Blackbox optimization".
Accanto a ogni fonte nell'elenco di riferimenti c'è un pulsante "Aggiungi alla bibliografia". Premilo e genereremo automaticamente la citazione bibliografica dell'opera scelta nello stile citazionale di cui hai bisogno: APA, MLA, Harvard, Chicago, Vancouver ecc.
Puoi anche scaricare il testo completo della pubblicazione scientifica nel formato .pdf e leggere online l'abstract (il sommario) dell'opera se è presente nei metadati.
Articoli di riviste sul tema "Blackbox optimization":
Audet, Charles, Sébastien Le Digabel e Mathilde Peyrega. "Linear equalities in blackbox optimization". Computational Optimization and Applications 61, n. 1 (19 ottobre 2014): 1–23. http://dx.doi.org/10.1007/s10589-014-9708-2.
Audet, Charles, J. E. Dennis e Sébastien Le Digabel. "Trade-off studies in blackbox optimization". Optimization Methods and Software 27, n. 4-5 (ottobre 2012): 613–24. http://dx.doi.org/10.1080/10556788.2011.571687.
Audet, Charles, Alain Batailly e Solène Kojtych. "Escaping Unknown Discontinuous Regions in Blackbox Optimization". SIAM Journal on Optimization 32, n. 3 (4 agosto 2022): 1843–70. http://dx.doi.org/10.1137/21m1420915.
Gramacy, Robert B., Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth Wells e Stefan M. Wild. "Modeling an Augmented Lagrangian for Blackbox Constrained Optimization". Technometrics 58, n. 1 (2 gennaio 2016): 1–11. http://dx.doi.org/10.1080/00401706.2015.1014065.
Chen, Hao, e William J. Welch. "Comment: Expected Improvement for Efficient Blackbox Constrained Optimization". Technometrics 58, n. 1 (2 gennaio 2016): 12–15. http://dx.doi.org/10.1080/00401706.2015.1044119.
Audet, Charles, Gilles Caporossi e Stéphane Jacquet. "Binary, unrelaxable and hidden constraints in blackbox optimization". Operations Research Letters 48, n. 4 (luglio 2020): 467–71. http://dx.doi.org/10.1016/j.orl.2020.05.011.
Audet, Charles, Jean Bigeon, Romain Couderc e Michael Kokkolaras. "Sequential stochastic blackbox optimization with zeroth-order gradient estimators". AIMS Mathematics 8, n. 11 (2023): 25922–56. http://dx.doi.org/10.3934/math.20231321.
Audet, Charles, e Michael Kokkolaras. "Blackbox and derivative-free optimization: theory, algorithms and applications". Optimization and Engineering 17, n. 1 (1 febbraio 2016): 1–2. http://dx.doi.org/10.1007/s11081-016-9307-4.
Herraz, Mahfoud, Jean-Max Redonnet, Mohammed Sbihi e Marcel Mongeau. "Blackbox optimization and surrogate models for machining free-form surfaces". Computers & Industrial Engineering 177 (marzo 2023): 109029. http://dx.doi.org/10.1016/j.cie.2023.109029.
Sankaran, Anush, Olivier Mastropietro, Ehsan Saboori, Yasser Idris, Davis Sawyer, MohammadHossein AskariHemmat e Ghouthi Boukli Hacene. "Deeplite NeutrinoTM: A BlackBox Framework for Constrained Deep Learning Model Optimization". Proceedings of the AAAI Conference on Artificial Intelligence 35, n. 17 (18 maggio 2021): 15166–74. http://dx.doi.org/10.1609/aaai.v35i17.17780.
Tesi sul tema "Blackbox optimization":
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.
Numerous industrial optimization problems are concerned with complex systems and have no explicit analytical formulation, that is they are blackbox optimization problems. They may be mixed, namely involve different types of variables (continuous and discrete), and comprise many constraints that must be satisfied. In addition, the objective and constraint blackbox functions may be computationally expensive to evaluate.In this thesis, we investigate solution methods for such challenging problems, i.e constrained mixed-variable blackbox optimization problems involving computationally expensive functions.As the use of derivatives is impractical, problems of this form are commonly tackled using derivative-free approaches such as evolutionary algorithms, direct search and surrogate-based methods.We investigate the performance of such deterministic and stochastic methods in the context of blackbox optimization, including a finite element test case designed for our research purposes. In particular, the performance of the ORTHOMADS instantiation of the direct search MADS algorithm is analyzed on continuous and mixed-integer optimization problems from the literature.We also propose a new blackbox optimization algorithm, called BOA, based on surrogate approximations. It proceeds in two phases, the first of which focuses on finding a feasible solution, while the second one iteratively improves the objective value of the best feasible solution found. Experiments on instances stemming from the literature and applications from the automotive industry are reported. They namely include results of our algorithm considering different types of surrogates and comparisons with ORTHOMADS
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.
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science
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.
The aim of the thesis is to develop algorithms for optimal maintenance scheduling. We focus on the specific case of large systems that consist of several components linked by a common stock of spare parts. The numerical experiments are carried out on systems of components from a single hydroelectric power plant.The first part is devoted to blackbox methods which are commonly used in maintenance scheduling. We focus on a kriging-based algorithm, Efficient Global Optimization (EGO), and on a direct search method, Mesh Adaptive Direct Search (MADS). We present a theoretical and practical review of the algorithms as well as some improvements for the implementation of EGO. MADS and EGO are compared on an academic benchmark and on small industrial maintenance problems, showing the superiority of MADS but also the limitation of the blackbox approach when tackling large-scale problems.In a second part, we want to take into account the fact that the system is composed of several components linked by a common stock in order to address large-scale maintenance optimization problems. For that purpose, we develop a model of the dynamics of the studied system and formulate an explicit stochastic optimal control problem. We set up a scheme of decomposition by prediction, based on the Auxiliary Problem Principle (APP), that turns the resolution of the large-scale problem into the iterative resolution of a sequence of subproblems of smaller size. The decomposition is first applied on synthetic test cases where it proves to be very efficient. For the industrial case, a "relaxation" of the system is needed and developed to apply the decomposition methodology. In the numerical experiments, we solve a Sample Average Approximation (SAA) of the problem and show that the decomposition leads to substantial gains over the reference algorithm.As we use a SAA method, we have considered the APP in a deterministic setting. In the third part, we study the APP in the stochastic approximation framework in a Banach space. We prove the measurability of the iterates of the algorithm, extend convergence results from Hilbert spaces to Banach spaces and give efficiency estimates
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.
Draheim, Patrick [Verfasser], Gabriel [Akademischer Betreuer] Zachmann, Gabriel [Gutachter] Zachmann e 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.
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.
We investigate various aspects of adaptive randomized (or stochastic) algorithms for both constrained and unconstrained black-box continuous optimization. The first part of this thesis focuses on step-size adaptation in unconstrained optimization. We first present a methodology for assessing efficiently a step-size adaptation mechanism that consists in testing a given algorithm on a minimal set of functions, each reflecting a particular difficulty that an efficient step-size adaptation algorithm should overcome. We then benchmark two step-size adaptation mechanisms on the well-known BBOB noiseless testbed and compare their performance to the one of the state-of-the-art evolution strategy (ES), CMA-ES, with cumulative step-size adaptation. In the second part of this thesis, we investigate linear convergence of a (1 + 1)-ES and a general step-size adaptive randomized algorithm on a linearly constrained optimization problem, where an adaptive augmented Lagrangian approach is used to handle the constraints. To that end, we extend the Markov chain approach used to analyze randomized algorithms for unconstrained optimization to the constrained case. We prove that when the augmented Lagrangian associated to the problem, centered at the optimum and the corresponding Lagrange multipliers, is positive homogeneous of degree 2, then for algorithms enjoying some invariance properties, there exists an underlying homogeneous Markov chain whose stability (typically positivity and Harris-recurrence) leads to linear convergence to both the optimum and the corresponding Lagrange multipliers. We deduce linear convergence under the aforementioned stability assumptions by applying a law of large numbers for Markov chains. We also present a general framework to design an augmented-Lagrangian-based adaptive randomized algorithm for constrained optimization, from an adaptive randomized algorithm for unconstrained optimization
Wang, Pei-Qi, e 王姵淇. "Apply Ant Colony Optimization to Test Case Prioritization for Blackbox Testing". Thesis, 2011. http://ndltd.ncl.edu.tw/handle/5uz6rz.
中原大學
工業與系統工程研究所
99
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 become an important issue. In such case, test case prioritization technique is one of effective technologies. Test case prioritizaiton techniques schedule test cases in an order that attempts to maximize the effectiveness in terms of meeting some performance goals. In previous literatures, test case prioritization techniques considered prioritization factors based on costs, requirements, number of faults, faults severities, and so on; the researches have never considered the inter-dependency between test cases. Unlike past researches of the test case prioritization problem, we proposed the test case prioritization techniques based on severity, complexity and inter-dependency in black-box testing. Based ant colony optimization, and combined with the Maximum Partial Order/Arbitrary Insertion(MPO/AI)method, we generated test case execution order with precedence constraints. In this study, the method we proposed to solve scheduling problem is compared with the other algorithms. To solve the SOP problem, experimantal results indicate that our proposed technique yields a comparable value to other algorithms, and execute time is shortened significantly. To solve the TCP problem, we obtain better result than the comparison algorithms. Using in practical application of our graphical user interface(GUI)testing, the testing effectiveness is superior to the comparison algorithms. Therefore, the experimental results show that, our proposed technique is useful to prioritizate test cases.
Libri sul tema "Blackbox optimization":
Audet, Charles, e Warren Hare. Derivative-Free and Blackbox Optimization. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5.
Audet, Charles, e Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2018.
Audet, Charles, e Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2017.
Capitoli di libri sul tema "Blackbox optimization":
Audet, Charles. "Blackbox Optimization". In Encyclopedia of Optimization, 1–6. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-54621-2_723-1.
Audet, Charles, e Warren Hare. "Biobjective Optimization". In Derivative-Free and Blackbox Optimization, 247–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_14.
Audet, Charles, e Warren Hare. "Optimization Using Surrogates and Models". In Derivative-Free and Blackbox Optimization, 235–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_13.
Audet, Charles, e Warren Hare. "Positive Bases and Nonsmooth Optimization". In Derivative-Free and Blackbox Optimization, 95–114. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_6.
Audet, Charles, e Warren Hare. "Introduction: Tools and Challenges in Derivative-Free and Blackbox Optimization". In Derivative-Free and Blackbox Optimization, 3–14. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_1.
Audet, Charles, e Warren Hare. "Model-Based Descent". In Derivative-Free and Blackbox Optimization, 183–200. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_10.
Audet, Charles, e Warren Hare. "Model-Based Trust Region". In Derivative-Free and Blackbox Optimization, 201–18. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_11.
Audet, Charles, e Warren Hare. "Variables and Constraints". In Derivative-Free and Blackbox Optimization, 221–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_12.
Audet, Charles, e Warren Hare. "Mathematical Background". In Derivative-Free and Blackbox Optimization, 15–31. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_2.
Audet, Charles, e Warren Hare. "The Beginnings of DFO Algorithms". In Derivative-Free and Blackbox Optimization, 33–54. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_3.
Atti di convegni sul tema "Blackbox optimization":
Hawkins, Byron, Brian Demsky e Michael B. Taylor. "BlackBox: lightweight security monitoring for COTS binaries". In CGO '16: 14th Annual IEEE/ACM International Symposium on Code Generation and Optimization. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2854038.2854062.
Dev, Rahul, Krishanu Kundu, Amrita Rai, Shiv Narain Gupta, Abhishek Kaushik e 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.
Kurhe, Vaibhav Kiran, Pratik Karia, Shubhani Gupta, Abhishek Rose e 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.
"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.
Hutter, Frank, Holger Hoos e Kevin Leyton-Brown. "An evaluation of sequential model-based optimization for expensive blackbox functions". In Proceeding of the fifteenth annual conference companion. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2501592.
Audet, Charles, Sébastien Le Digabel, Ludovic Salomon e Christophe Tribes. "Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite". In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3534019.
Tehrani, H. Mazaheri, A. Frances, R. Asensi e 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.
Hartpence, Bruce, e 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.
Chamseddine, Ibrahim M., e 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.
Wang, Lv, Teng Long, Lei Peng e 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.