Bibliografías temáticas / Machine learning, Global Optimization

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Literatura académica sobre el tema "Machine learning, Global Optimization"

Autor: Grafiati
Publicado: 10 de marzo de 2023

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Artículos de revistas sobre el tema "Machine learning, Global Optimization"

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Cassioli, A., D. Di Lorenzo, M. Locatelli, F. Schoen, and M. Sciandrone. "Machine learning for global optimization." Computational Optimization and Applications 51, no. 1 (2010): 279–303. http://dx.doi.org/10.1007/s10589-010-9330-x.

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Kudyshev, Zhaxylyk A., Alexander V. Kildishev, Vladimir M. Shalaev, and Alexandra Boltasseva. "Machine learning–assisted global optimization of photonic devices." Nanophotonics 10, no. 1 (2020): 371–83. http://dx.doi.org/10.1515/nanoph-2020-0376.

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AbstractOver the past decade, artificially engineered optical materials and nanostructured thin films have revolutionized the area of photonics by employing novel concepts of metamaterials and metasurfaces where spatially varying structures yield tailorable “by design” effective electromagnetic properties. The current state-of-the-art approach to designing and optimizing such structures relies heavily on simplistic, intuitive shapes for their unit cells or metaatoms. Such an approach cannot provide the global solution to a complex optimization problem where metaatom shape, in-plane geometry, o
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Abdul Salam, Mustafa, Ahmad Taher Azar, and Rana Hussien. "Swarm-Based Extreme Learning Machine Models for Global Optimization." Computers, Materials & Continua 70, no. 3 (2022): 6339–63. http://dx.doi.org/10.32604/cmc.2022.020583.

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TAKAMATSU, Ryosuke, and Wataru YAMAZAKI. "Global topology optimization of supersonic airfoil using machine learning technologies." Proceedings of The Computational Mechanics Conference 2021.34 (2021): 112. http://dx.doi.org/10.1299/jsmecmd.2021.34.112.

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Tsoulos, Ioannis G., Alexandros Tzallas, Evangelos Karvounis, and Dimitrios Tsalikakis. "NeuralMinimizer: A Novel Method for Global Optimization." Information 14, no. 2 (2023): 66. http://dx.doi.org/10.3390/info14020066.

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The problem of finding the global minimum of multidimensional functions is often applied to a wide range of problems. An innovative method of finding the global minimum of multidimensional functions is presented here. This method first generates an approximation of the objective function using only a few real samples from it. These samples construct the approach using a machine learning model. Next, the required sampling is performed by the approximation function. Furthermore, the approach is improved on each sample by using found local minima as samples for the training set of the machine lea
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Honda, M., and E. Narita. "Machine-learning assisted steady-state profile predictions using global optimization techniques." Physics of Plasmas 26, no. 10 (2019): 102307. http://dx.doi.org/10.1063/1.5117846.

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Wu, Shaohua, Yong Hu, Wei Wang, Xinyong Feng, and Wanneng Shu. "Application of Global Optimization Methods for Feature Selection and Machine Learning." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/241517.

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The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. The process reduces the number of features by removing irrelevant and redundant data. This paper proposed a novel immune clonal genetic algorithm based on immune clonal algorithm designed to solve the feature selection problem. The proposed algorithm has more exploration and exploitation abilities due to the clonal selection theory, and each antibody in the search space specifies a subset of the possible features. Experimental results show that the proposed algorithm simplifies the fe
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Ma, Sicong, Cheng Shang, Chuan-Ming Wang, and Zhi-Pan Liu. "Thermodynamic rules for zeolite formation from machine learning based global optimization." Chemical Science 11, no. 37 (2020): 10113–18. http://dx.doi.org/10.1039/d0sc03918g.

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Machine learning based atomic simulation explores more than one million minima from global potential energy surface of SiAlPO system, and identifies thermodynamics rules on energetics, framework and composition for stable zeolite.
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Huang, Si-Da, Cheng Shang, Pei-Lin Kang, and Zhi-Pan Liu. "Atomic structure of boron resolved using machine learning and global sampling." Chemical Science 9, no. 46 (2018): 8644–55. http://dx.doi.org/10.1039/c8sc03427c.

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Barkalov, Konstantin, Ilya Lebedev, and Evgeny Kozinov. "Acceleration of Global Optimization Algorithm by Detecting Local Extrema Based on Machine Learning." Entropy 23, no. 10 (2021): 1272. http://dx.doi.org/10.3390/e23101272.

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This paper features the study of global optimization problems and numerical methods of their solution. Such problems are computationally expensive since the objective function can be multi-extremal, nondifferentiable, and, as a rule, given in the form of a “black box”. This study used a deterministic algorithm for finding the global extremum. This algorithm is based neither on the concept of multistart, nor nature-inspired algorithms. The article provides computational rules of the one-dimensional algorithm and the nested optimization scheme which could be applied for solving multidimensional
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Tesis sobre el tema "Machine learning, Global Optimization"

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Nowak, Hans II(Hans Antoon). "Strategic capacity planning using data science, optimization, and machine learning." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/126914.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 101-104).<br>Raytheon's Circuit Card Assembly (CCA) factory in Andover, MA is Raytheon's largest factory and the largest Department of Defense (DOD) CCA manufacturer in th
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Veluscek, Marco. "Global supply chain optimization : a machine learning perspective to improve caterpillar's logistics operations." Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/13050.

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Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the problem of supply chain optimization. However, such techniques a
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Schweidtmann, Artur M. [Verfasser], Alexander [Akademischer Betreuer] Mitsos, and Andreas [Akademischer Betreuer] Schuppert. "Global optimization of processes through machine learning / Artur M. Schweidtmann ; Alexander Mitsos, Andreas Schuppert." Aachen : Universitätsbibliothek der RWTH Aachen, 2021. http://d-nb.info/1240690924/34.

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Taheri, Mehdi. "Machine Learning from Computer Simulations with Applications in Rail Vehicle Dynamics and System Identification." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81417.

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The application of stochastic modeling for learning the behavior of multibody dynamics models is investigated. The stochastic modeling technique is also known as Kriging or random function approach. Post-processing data from a simulation run is used to train the stochastic model that estimates the relationship between model inputs, such as the suspension relative displacement and velocity, and the output, for example, sum of suspension forces. Computational efficiency of Multibody Dynamics (MBD) models can be improved by replacing their computationally-intensive subsystems with stochastic pred
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Gabere, Musa Nur. "Prediction of antimicrobial peptides using hyperparameter optimized support vector machines." Thesis, University of the Western Cape, 2011. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_7345_1330684697.

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<p>Antimicrobial peptides (AMPs) play a key role in the innate immune response. They can be ubiquitously found in a wide range of eukaryotes including mammals, amphibians, insects, plants, and protozoa. In lower organisms, AMPs function merely as antibiotics by permeabilizing cell membranes and lysing invading microbes. Prediction of antimicrobial peptides is important because experimental methods used in characterizing AMPs are costly, time consuming and resource intensive and identification of AMPs in insects can serve as a template for the design of novel antibiotic. In order to fulfil this
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Belkhir, Nacim. "Per Instance Algorithm Configuration for Continuous Black Box Optimization." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS455/document.

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Cette thèse porte sur la configurationAutomatisée des algorithmes qui vise à trouver le meilleur paramétrage à un problème donné ou une catégorie deproblèmes.Le problème de configuration de l'algorithme revient doncà un problème de métaFoptimisation dans l'espace desparamètres, dont le métaFobjectif est la mesure deperformance de l’algorithme donné avec une configuration de paramètres donnée.Des approches plus récentes reposent sur une description des problèmes et ont pour but d’apprendre la relationentre l’espace des caractéristiques des problèmes etl’espace des configurations de l’algorithme
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Liu, Liu. "Stochastic Optimization in Machine Learning." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/19982.

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Stochastic optimization has received extensive attention in recent years due to their extremely potential for solving the large-scale optimization problem. However, the classical optimization algorithm and original stochastic method might prove to be inefficient due to the fact that: 1) the cost-per-iteration is a computational challenge, 2) the convergence and complexity are poorly performed. In this thesis, we exploit the stochastic optimization from three kinds of "order" optimization to address the problem. For the stochastic zero-order optimization, we introduce a novel variance reduction
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Leblond, Rémi. "Asynchronous optimization for machine learning." Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEE057/document.

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Les explosions combinées de la puissance computationnelle et de la quantité de données disponibles ont fait des algorithmes les nouveaux facteurs limitants en machine learning. L’objectif de cette thèse est donc d’introduire de nouvelles méthodes capables de tirer profit de quantités de données et de ressources computationnelles importantes. Nous présentons deux contributions indépendantes. Premièrement, nous développons des algorithmes d’optimisation rapides, adaptés aux avancées en architecture de calcul parallèle pour traiter des quantités massives de données. Nous introduisons un cadre d’a
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Bai, Hao. "Machine learning assisted probabilistic prediction of long-term fatigue damage and vibration reduction of wind turbine tower using active damping system." Thesis, Normandie, 2021. http://www.theses.fr/2021NORMIR01.

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Cette thèse est consacrée au développement d'un système d'amortissement actif pour la réduction des vibrations du mât d'éoliennes en cas de vent avec rafales et de vent avec turbulence. La présence de vibrations entraîne souvent soit une déflexion ultime au sommet du mât d'éolienne, soit une défaillance due à la fatigue du matériau près du bas du mât d'éolienne. De plus, étant donné la nature aléatoire de l'état du vent, il est indispensable d'examiner ce problème d'un point de vue probabiliste. Dans ce travail, un cadre probabiliste d'analyse de la fatigue est développé et amélioré en utilisa
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Chang, Allison An. "Integer optimization methods for machine learning." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72643.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (p. 129-137).<br>In this thesis, we propose new mixed integer optimization (MIO) methods to ad- dress problems in machine learning. The first part develops methods for supervised bipartite ranking, which arises in prioritization tasks
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Libros sobre el tema "Machine learning, Global Optimization"

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Optimization for machine learning. MIT Press, 2012.

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Lin, Zhouchen, Huan Li, and Cong Fang. Accelerated Optimization for Machine Learning. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2910-8.

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Agrawal, Tanay. Hyperparameter Optimization in Machine Learning. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-6579-6.

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Fazelnia, Ghazal. Optimization for Probabilistic Machine Learning. [publisher not identified], 2019.

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Nicosia, Giuseppe, Varun Ojha, Emanuele La Malfa, et al., eds. Machine Learning, Optimization, and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95470-3.

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Nicosia, Giuseppe, Varun Ojha, Emanuele La Malfa, et al., eds. Machine Learning, Optimization, and Data Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95467-3.

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Jiang, Jiawei, Bin Cui, and Ce Zhang. Distributed Machine Learning and Gradient Optimization. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-3420-8.

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Pardalos, Panos, Mario Pavone, Giovanni Maria Farinella, and Vincenzo Cutello, eds. Machine Learning, Optimization, and Big Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27926-8.

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Nicosia, Giuseppe, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, and Vincenzo Sciacca, eds. Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7.

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Kulkarni, Anand J., and Suresh Chandra Satapathy, eds. Optimization in Machine Learning and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0994-0.

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Capítulos de libros sobre el tema "Machine learning, Global Optimization"

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Kearfott, Ralph Baker. "Mathematically Rigorous Global Optimization and Fuzzy Optimization." In Black Box Optimization, Machine Learning, and No-Free Lunch Theorems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66515-9_7.

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de Winter, Roy, Bas van Stein, Matthys Dijkman, and Thomas Bäck. "Designing Ships Using Constrained Multi-objective Efficient Global Optimization." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13709-0_16.

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Cocola, Jorio, and Paul Hand. "Global Convergence of Sobolev Training for Overparameterized Neural Networks." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_51.

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Zabinsky, Zelda B., Giulia Pedrielli, and Hao Huang. "A Framework for Multi-fidelity Modeling in Global Optimization Approaches." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_28.

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Griewank, Andreas, and Ángel Rojas. "Treating Artificial Neural Net Training as a Nonsmooth Global Optimization Problem." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_64.

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Issa, Mohamed, Aboul Ella Hassanien, and Ibrahim Ziedan. "Performance Evaluation of Sine-Cosine Optimization Versus Particle Swarm Optimization for Global Sequence Alignment Problem." In Machine Learning Paradigms: Theory and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02357-7_18.

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Wang, Yong-Jun, Jiang-She Zhang, and Yu-Fen Zhang. "An Effective and Efficient Two Stage Algorithm for Global Optimization." In Advances in Machine Learning and Cybernetics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_51.

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Kiranyaz, Serkan, Turker Ince, and Moncef Gabbouj. "Improving Global Convergence." In Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37846-1_5.

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Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_18.

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AbstractThe Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.
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Rodrigues, Douglas, Gustavo Henrique de Rosa, Leandro Aparecido Passos, and João Paulo Papa. "Adaptive Improved Flower Pollination Algorithm for Global Optimization." In Nature-Inspired Computation in Data Mining and Machine Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28553-1_1.

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Actas de conferencias sobre el tema "Machine learning, Global Optimization"

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He, Yi-chao, and Kun-qi Liu. "A Modified Particle Swarm Optimization for Solving Global Optimization Problems." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258615.

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Tamura, Kenichi, and Keiichiro Yasuda. "Spiral Multipoint Search for Global Optimization." In 2011 Tenth International Conference on Machine Learning and Applications (ICMLA). IEEE, 2011. http://dx.doi.org/10.1109/icmla.2011.131.

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Yong-Jun Wang, Jiang-She Zhang, and Yu-Fen Zhang. "A fast hybrid algorithm for global optimization." In Proceedings of 2005 International Conference on Machine Learning and Cybernetics. IEEE, 2005. http://dx.doi.org/10.1109/icmlc.2005.1527462.

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Sun, Gao-Ji. "A new evolutionary algorithm for global numerical optimization." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580961.

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Nacef, Abdelhakim, Miloud Bagaa, Youcef Aklouf, Abdellah Kaci, Diego Leonel Cadette Dutra, and Adlen Ksentini. "Self-optimized network: When Machine Learning Meets Optimization." In GLOBECOM 2021 - 2021 IEEE Global Communications Conference. IEEE, 2021. http://dx.doi.org/10.1109/globecom46510.2021.9685681.

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Li, Xue-Qiang, Zhi-Feng Hao, and Han Huang. "An evolutionary algorithm with sorted race mechanism for global optimization." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580810.

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Injadat, MohammadNoor, Fadi Salo, Ali Bou Nassif, Aleksander Essex, and Abdallah Shami. "Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection." In GLOBECOM 2018 - 2018 IEEE Global Communications Conference. IEEE, 2018. http://dx.doi.org/10.1109/glocom.2018.8647714.

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Candelieri, Antonio, and Francesco Archetti. "Sequential model based optimization with black-box constraints: Feasibility determination via machine learning." In PROCEEDINGS LEGO – 14TH INTERNATIONAL GLOBAL OPTIMIZATION WORKSHOP. Author(s), 2019. http://dx.doi.org/10.1063/1.5089977.

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Chen, Chang-Huang. "Bare bone particle swarm optimization with integration of global and local learning strategies." In 2011 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2011. http://dx.doi.org/10.1109/icmlc.2011.6016781.

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Soroush, H. M. "Bicriteria single machine scheduling with setup times and learning effects." In PROCEEDINGS OF THE SIXTH GLOBAL CONFERENCE ON POWER CONTROL AND OPTIMIZATION. AIP, 2012. http://dx.doi.org/10.1063/1.4769005.

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Informes sobre el tema "Machine learning, Global Optimization"

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Saenz, Juan Antonio, Ismael Djibrilla Boureima, Vitaliy Gyrya, and Susan Kurien. Machine-Learning for Rapid Optimization of Turbulence Models. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1638623.

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Gu, Xiaofeng, A. Fedotov, and D. Kayran. Application of a machine learning algorithm (XGBoost) to offline RHIC luminosity optimization. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1777441.

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Rolf, Esther, Jonathan Proctor, Tamma Carleton, et al. A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery. National Bureau of Economic Research, 2020. http://dx.doi.org/10.3386/w28045.

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Scheinberg, Katya. Derivative Free Optimization of Complex Systems with the Use of Statistical Machine Learning Models. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada622645.

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Ghanshyam, Pilania, Kenneth James McClellan, Christopher Richard Stanek, and Blas P. Uberuaga. Physics-Informed Machine Learning for Discovery and Optimization of Materials: A Case Study of Scintillators. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1463529.

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Bao, Jie, Chao Wang, Zhijie Xu, and Brian J. Koeppel. Physics-Informed Machine Learning with Application to Solid Oxide Fuel Cell System Modeling and Optimization. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1569289.

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Gabelmann, Jeffrey, and Eduardo Gildin. A Machine Learning-Based Geothermal Drilling Optimization System Using EM Short-Hop Bit Dynamics Measurements. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1842454.

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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Vittorio, Alan, and Kate Calvin. Using machine learning to improve land use/cover characterization and projection for scenario-based global modeling. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769796.

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Wu, S. Boiler Optimization Using Advance Machine Learning Techniques. Final Report for period September 30, 1995 - September 29, 2000. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/877237.

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