Littérature scientifique sur le sujet « Blackbox optimization »
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Articles de revues sur le sujet "Blackbox optimization"
Audet, Charles, Sébastien Le Digabel et Mathilde Peyrega. « Linear equalities in blackbox optimization ». Computational Optimization and Applications 61, no 1 (19 octobre 2014) : 1–23. http://dx.doi.org/10.1007/s10589-014-9708-2.
Texte intégralAudet, Charles, J. E. Dennis et Sébastien Le Digabel. « Trade-off studies in blackbox optimization ». Optimization Methods and Software 27, no 4-5 (octobre 2012) : 613–24. http://dx.doi.org/10.1080/10556788.2011.571687.
Texte intégralAudet, Charles, Alain Batailly et Solène Kojtych. « Escaping Unknown Discontinuous Regions in Blackbox Optimization ». SIAM Journal on Optimization 32, no 3 (4 août 2022) : 1843–70. http://dx.doi.org/10.1137/21m1420915.
Texte intégralGramacy, Robert B., Genetha A. Gray, Sébastien Le Digabel, Herbert K. H. Lee, Pritam Ranjan, Garth Wells et Stefan M. Wild. « Modeling an Augmented Lagrangian for Blackbox Constrained Optimization ». Technometrics 58, no 1 (2 janvier 2016) : 1–11. http://dx.doi.org/10.1080/00401706.2015.1014065.
Texte intégralChen, Hao, et William J. Welch. « Comment : Expected Improvement for Efficient Blackbox Constrained Optimization ». Technometrics 58, no 1 (2 janvier 2016) : 12–15. http://dx.doi.org/10.1080/00401706.2015.1044119.
Texte intégralAudet, Charles, Gilles Caporossi et Stéphane Jacquet. « Binary, unrelaxable and hidden constraints in blackbox optimization ». Operations Research Letters 48, no 4 (juillet 2020) : 467–71. http://dx.doi.org/10.1016/j.orl.2020.05.011.
Texte intégralAudet, Charles, Jean Bigeon, Romain Couderc et 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.
Texte intégralAudet, Charles, et Michael Kokkolaras. « Blackbox and derivative-free optimization : theory, algorithms and applications ». Optimization and Engineering 17, no 1 (1 février 2016) : 1–2. http://dx.doi.org/10.1007/s11081-016-9307-4.
Texte intégralHerraz, Mahfoud, Jean-Max Redonnet, Mohammed Sbihi et Marcel Mongeau. « Blackbox optimization and surrogate models for machining free-form surfaces ». Computers & ; Industrial Engineering 177 (mars 2023) : 109029. http://dx.doi.org/10.1016/j.cie.2023.109029.
Texte intégralSankaran, Anush, Olivier Mastropietro, Ehsan Saboori, Yasser Idris, Davis Sawyer, MohammadHossein AskariHemmat et Ghouthi Boukli Hacene. « Deeplite NeutrinoTM : A BlackBox Framework for Constrained Deep Learning Model Optimization ». Proceedings of the AAAI Conference on Artificial Intelligence 35, no 17 (18 mai 2021) : 15166–74. http://dx.doi.org/10.1609/aaai.v35i17.17780.
Texte intégralThèses sur le sujet "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.
Texte intégralNumerous 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.
Texte intégralPh.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.
Texte intégralThe 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.
Texte intégralDraheim, Patrick [Verfasser], Gabriel [Akademischer Betreuer] Zachmann, Gabriel [Gutachter] Zachmann et 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.
Texte intégralAtamna, Asma. « Analysis of Randomized Adaptive Algorithms for Black-Box Continuous Constrained Optimization ». Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS010/document.
Texte intégralWe 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, et 王姵淇. « Apply Ant Colony Optimization to Test Case Prioritization for Blackbox Testing ». Thesis, 2011. http://ndltd.ncl.edu.tw/handle/5uz6rz.
Texte intégral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.
Livres sur le sujet "Blackbox optimization"
Audet, Charles, et Warren Hare. Derivative-Free and Blackbox Optimization. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5.
Texte intégralAudet, Charles, et Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2018.
Trouver le texte intégralAudet, Charles, et Warren Hare. Derivative-Free and Blackbox Optimization. Springer, 2017.
Trouver le texte intégralChapitres de livres sur le sujet "Blackbox optimization"
Audet, Charles. « Blackbox Optimization ». Dans Encyclopedia of Optimization, 1–6. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-54621-2_723-1.
Texte intégralAudet, Charles, et Warren Hare. « Biobjective Optimization ». Dans Derivative-Free and Blackbox Optimization, 247–62. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_14.
Texte intégralAudet, Charles, et Warren Hare. « Optimization Using Surrogates and Models ». Dans Derivative-Free and Blackbox Optimization, 235–46. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_13.
Texte intégralAudet, Charles, et Warren Hare. « Positive Bases and Nonsmooth Optimization ». Dans Derivative-Free and Blackbox Optimization, 95–114. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_6.
Texte intégralAudet, Charles, et Warren Hare. « Introduction : Tools and Challenges in Derivative-Free and Blackbox Optimization ». Dans Derivative-Free and Blackbox Optimization, 3–14. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_1.
Texte intégralAudet, Charles, et Warren Hare. « Model-Based Descent ». Dans Derivative-Free and Blackbox Optimization, 183–200. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_10.
Texte intégralAudet, Charles, et Warren Hare. « Model-Based Trust Region ». Dans Derivative-Free and Blackbox Optimization, 201–18. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_11.
Texte intégralAudet, Charles, et Warren Hare. « Variables and Constraints ». Dans Derivative-Free and Blackbox Optimization, 221–34. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_12.
Texte intégralAudet, Charles, et Warren Hare. « Mathematical Background ». Dans Derivative-Free and Blackbox Optimization, 15–31. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_2.
Texte intégralAudet, Charles, et Warren Hare. « The Beginnings of DFO Algorithms ». Dans Derivative-Free and Blackbox Optimization, 33–54. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68913-5_3.
Texte intégralActes de conférences sur le sujet "Blackbox optimization"
Hawkins, Byron, Brian Demsky et Michael B. Taylor. « BlackBox : lightweight security monitoring for COTS binaries ». Dans 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.
Texte intégralDev, Rahul, Krishanu Kundu, Amrita Rai, Shiv Narain Gupta, Abhishek Kaushik et Reshu Agarwal. « Design and Implementation of Blackbox in Vehicles ». Dans 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.
Texte intégralKurhe, Vaibhav Kiran, Pratik Karia, Shubhani Gupta, Abhishek Rose et Sorav Bansal. « Automatic Generation of Debug Headers through BlackBox Equivalence Checking ». Dans 2022 IEEE/ACM International Symposium on Code Generation and Optimization (CGO). IEEE, 2022. http://dx.doi.org/10.1109/cgo53902.2022.9741273.
Texte intégral« Managing Computationally Expensive Blackbox Multiobjective Optimization Problems with Libensemble ». Dans 2020 Spring Simulation Conference. Society for Modeling and Simulation International (SCS), 2020. http://dx.doi.org/10.22360/springsim.2020.hpc.001.
Texte intégralHutter, Frank, Holger Hoos et Kevin Leyton-Brown. « An evaluation of sequential model-based optimization for expensive blackbox functions ». Dans Proceeding of the fifteenth annual conference companion. New York, New York, USA : ACM Press, 2013. http://dx.doi.org/10.1145/2464576.2501592.
Texte intégralAudet, Charles, Sébastien Le Digabel, Ludovic Salomon et Christophe Tribes. « Constrained blackbox optimization with the NOMAD solver on the COCO constrained test suite ». Dans GECCO '22 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2022. http://dx.doi.org/10.1145/3520304.3534019.
Texte intégralTehrani, H. Mazaheri, A. Frances, R. Asensi et J. Uceda. « Blackbox Equivalent Switching Model Identification of DC-DC Power Electronic Converters Using optimization Algorithms ». Dans 2021 IEEE Fourth International Conference on DC Microgrids (ICDCM). IEEE, 2021. http://dx.doi.org/10.1109/icdcm50975.2021.9504611.
Texte intégralHartpence, Bruce, et Andres Kwasinski. « Considering the Blackbox : An Investigation of Optimization Techniques with Completely Balanced Datasets of Packet Traffic ». Dans 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9006508.
Texte intégralChamseddine, Ibrahim M., et Michael Kokkolaras. « Bio-Inspired Heuristic for Decoupling Network Configuration in Air Transportation System-of-Systems Design Optimization ». Dans 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.
Texte intégralWang, Lv, Teng Long, Lei Peng et Li Liu. « Optimized Radial Basis Function Metamodel for Expensive Engineering Design Optimization ». Dans ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87489.
Texte intégral