Literatura académica sobre el tema "Adaptive Penalty"
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Artículos de revistas sobre el tema "Adaptive Penalty"
Zhou, Zhenhua y Haijun Wu. "Convergence and Quasi-Optimality of an Adaptive Multi-Penalty Discontinuous Galerkin Method". Numerical Mathematics: Theory, Methods and Applications 9, n.º 1 (febrero de 2016): 51–86. http://dx.doi.org/10.4208/nmtma.2015.m1412.
Texto completoLiu, Yufeng, Hao Helen Zhang, Cheolwoo Park y Jeongyoun Ahn. "Support vector machines with adaptive penalty". Computational Statistics & Data Analysis 51, n.º 12 (agosto de 2007): 6380–94. http://dx.doi.org/10.1016/j.csda.2007.02.006.
Texto completoGosain, Anjana y Kavita Sachdeva. "Handling Constraints Using Penalty Functions in Materialized View Selection". International Journal of Natural Computing Research 8, n.º 2 (abril de 2019): 1–17. http://dx.doi.org/10.4018/ijncr.2019040101.
Texto completoLa Rivière, Patrick J., Junguo Bian y Phillip A. Vargas. "Comparison of Quadratic- and Median-Based Roughness Penalties for Penalized-Likelihood Sinogram Restoration in Computed Tomography". International Journal of Biomedical Imaging 2006 (2006): 1–7. http://dx.doi.org/10.1155/ijbi/2006/41380.
Texto completoLambert-Lacroix, Sophie y Laurent Zwald. "The adaptive BerHu penalty in robust regression". Journal of Nonparametric Statistics 28, n.º 3 (13 de junio de 2016): 487–514. http://dx.doi.org/10.1080/10485252.2016.1190359.
Texto completoMarchetti, Yuliya y Qing Zhou. "Solution path clustering with adaptive concave penalty". Electronic Journal of Statistics 8, n.º 1 (2014): 1569–603. http://dx.doi.org/10.1214/14-ejs934.
Texto completoTowfic, Zaid J. y Ali H. Sayed. "Adaptive Penalty-Based Distributed Stochastic Convex Optimization". IEEE Transactions on Signal Processing 62, n.º 15 (agosto de 2014): 3924–38. http://dx.doi.org/10.1109/tsp.2014.2331615.
Texto completoYu, Xinghuo y Baolin Wu. "An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming". Journal of Advanced Computational Intelligence and Intelligent Informatics 4, n.º 2 (20 de marzo de 2000): 164–70. http://dx.doi.org/10.20965/jaciii.2000.p0164.
Texto completoYEH, CHANG-CHING, KUEI-CHUNG CHANG, TIEN-FU CHEN y CHINGWEI YEH. "ADAPTIVE PIPELINE VOLTAGE SCALING IN HIGH PERFORMANCE MICROPROCESSOR". Journal of Circuits, Systems and Computers 19, n.º 08 (diciembre de 2010): 1817–34. http://dx.doi.org/10.1142/s0218126610007146.
Texto completoWang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis". Shock and Vibration 2021 (11 de noviembre de 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.
Texto completoTesis sobre el tema "Adaptive Penalty"
Li, Jonathan Chi Fai. "Eye closure penalty based signal quality metric for intelligent all-optical networks /". Connect to thesis, 2009. http://repository.unimelb.edu.au/10187/7047.
Texto completoSarkar, Abhishek. "The Gambler's Fallacy and Hot Outcome: Cognitive Biases or Adaptive Thinking for Goalkeepers' Decisions on Dive Direction During Penalty Shootouts". Bowling Green State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1483529030818181.
Texto completoSvensson, Daniel. "Generaliseringsförmåga vid genetisk programmering". Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-789.
Texto completoI detta arbete undersöks hur bestraffningsmetoder för att bestraffa storleken på GP-program påverkar generaliseringsförmågan. Arbetet grundar sig på ett arbete som Cavaretta och Chellapilla gjort, där de undersöker skillnaden i generaliseringsförmåga mellan bestraffningsmetoden ”Complexity Penalty functions” och ingen bestraffningsmetod.
I detta arbete har nya experiment gjorts med ”Complexity Penalty functions” och ”Adaptive parsimony pressure”, som är en annan bestraffningsmetod. Dessa bestraffningsmetoder har undersökts i samma domän som Cavaretta och Chellapilla och ytterligare i en domän för att ge en bättre bild av hur de generaliserar.
I arbetet visar det sig att användningen av någon av bestraffningsmetoderna ”Complexity Penalty functions” och ”Adaptive parsimony pressure” oftast ger bättre generaliseringsförmåga hos GP-program. Detta motsäger det Cavaretta och Chellapilla kommer fram till i sitt arbete. ”Adaptive parsimony pressure” verkar också vara bättre på att generalisera än ”Complexity Penalty functions”.
Yeh, Kuo-Chih y 葉國智. "Differential Evolution Algorithm with Adaptive Penalty for Constrained Continuous Global Optimization". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78691247950621499584.
Texto completo元智大學
工業工程與管理學系
95
The applications of Metaheuristic algorithms that used to solve optimization problems in researches are very popular, but most of them were generally for unconstrained optimization procedures. However, these normally exiting problems in real-world are always under constrained. This paper presents a differential-evolution-type algorithm for solving constrained continuous optimization problems. The proposed differential evolution (DE) algorithm is developed based upon the penalty function approach, where constraint violation is penalized by placing the constraints into the objective function. Penalty functions can deal both with equality and inequality constraints; in this study, equality constraints are transformed into inequality ones. In addition, to handle infeasibility during DE search, a random re-initialization procedure is executed to produce a new potential solution inside the allowable ranges. Three different types of increasing penalty factors and one adaptive penalty that adjust by constraint violations are compared for their performance on convergence. A dynamic tolerance allowed of equality constraints is executed, too. The performance measure includes the best objective value achieved and the number of function evaluations required. The recommendation for the selection of parameter setting in the new algorithm is given through a series of simulation optimizations and analysis by the design of experiments (DOE). The experimental results obtained by solving a variety of benchmark functions are used to demonstrate the effectiveness and efficiency of the penalty-function DE algorithm.
Joung, Ming-Huei y 鐘銘輝. "Research and Programming of Constrained Optimization Problem by Penalty Function and Adaptive Lagrange Function". Thesis, 1993. http://ndltd.ncl.edu.tw/handle/02081783409800506940.
Texto completo國立臺灣大學
土木工程研究所
81
In this paper, four new optimization methods are presented. First, Bezier Curve is used as line-searching direction. Second, Hyperbola is used in optima fitting. Third, a new penalty function is proposed in Penalty Method. Fourth, Adaptive Lagrange Function is proposed to solve constrained opimization problem. With the good behavior of being defined everywhere, accurate and differentiable, the Ideal Penalty Function can be used in all of the optimization methods. In the Adaptive Lagrange Method, a quadratic term is added to the saddle point of the original Lagrange function, and the Hessian matrix of the adaptive function will be positive definite. After that, the line-searching method can be used to search the optimal point, and only one unconstrained optimization iteration is required. The program based on both the new methods of this paper and the common used methods is set up with the characteristics of effectiveness, friendliness and completeness. Furthermore, the linkage program of engineering package and the optimization program is included and the user could set up the optimization package without being interfered in the overall engineering program. Several numerical examples are provided to illustrate the he package.
Garg, Vikram Vinod 1985. "Coupled flow systems, adjoint techniques and uncertainty quantification". Thesis, 2012. http://hdl.handle.net/2152/ETD-UT-2012-08-6034.
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Libros sobre el tema "Adaptive Penalty"
Gordon, Gregory S. Adopting Incitement to Commit War Crimes. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780190612689.003.0011.
Texto completoCapítulos de libros sobre el tema "Adaptive Penalty"
Huerta-Amante, Daniel Ángel y Hugo Terashima-Marín. "Adaptive Penalty Weights When Solving Congress Timetabling". En Advances in Artificial Intelligence – IBERAMIA 2004, 144–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30498-2_15.
Texto completoBarbosa, Helio J. C. y Afonso C. C. Lemonge. "An Adaptive Penalty Scheme for Steady-State Genetic Algorithms". En Genetic and Evolutionary Computation — GECCO 2003, 718–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45105-6_87.
Texto completoBoon, W. M. y J. M. Nordbotten. "An Adaptive Penalty Method for Inequality Constrained Minimization Problems". En Lecture Notes in Computational Science and Engineering, 155–64. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55874-1_14.
Texto completoGao, Lei y William F. Rosenberger. "Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response". En Contributions to Statistics, 91–98. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00218-7_11.
Texto completoBarbosa, Helio J. C., Afonso C. C. Lemonge y Heder S. Bernardino. "A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation". En Infosys Science Foundation Series, 1–27. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2184-5_1.
Texto completoKale, Ishaan R. y Anand J. Kulkarni. "Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach". En Constraint Handling in Cohort Intelligence Algorithm, 49–59. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003245193-4.
Texto completoRoss, Peter y Emma Hart. "An adaptive mutation scheme for a penalty-based graph-colouring GA". En Lecture Notes in Computer Science, 795–802. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056921.
Texto completoYen, Gary G. "An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization". En Constraint-Handling in Evolutionary Optimization, 121–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00619-7_6.
Texto completoChenggang, Cui, Yang Xiaofei y Gao Tingyu. "A Self-adaptive Interior Penalty Based Differential Evolution Algorithm for Constrained Optimization". En Lecture Notes in Computer Science, 309–18. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_37.
Texto completoChen, Wentao y Fei Han. "An Improved Multi-objective Particle Swarm Optimization with Adaptive Penalty Value for Feature Selection". En Communications in Computer and Information Science, 649–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_51.
Texto completoActas de conferencias sobre el tema "Adaptive Penalty"
Fathy, Mohammed y Michael Rotkowitz. "Essential Matrix Estimation Using Adaptive Penalty Formulations". En British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.50.
Texto completoNunez-Martinez, Jose y Josep Mangues-Bafalluy. "Distributed Lyapunov drift-plus-penalty routing for WiFi mesh networks with adaptive penalty weight". En 2012 IEEE Thirteenth International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). IEEE, 2012. http://dx.doi.org/10.1109/wowmom.2012.6263779.
Texto completoTakahama, Tetsuyuki y Setsuko Sakai. "An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization". En 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790360.
Texto completoKronvall, Ted, Filip Elvander, Stefan Ingi Adalbjornsson y Andreas Jakobsson. "An adaptive penalty approach to multi-pitch estimation". En 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362339.
Texto completoXinchao Li, Qianhua He, Yanxiong Li, Changbin Li y Zhingfeng Wang. "An adaptive premium penalty ant colony optimization algorithm". En 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890509.
Texto completoWang, Shiyuan, Yunfei Zheng y Shukai Duan. "Sparse Huber adaptive filter with correntropy induced metric penalty". En 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554125.
Texto completoAlaboudi, Dheyaa y Ali Alkenani. "Sparse sliced inverse regression based on adaptive lasso penalty". En PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0093717.
Texto completoDjebedjian, Berge, Ashraf Yaseen y Magdy Abou Rayan. "A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems". En 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10235.
Texto completoKramer, Oliver, Uli Schlachter y Valentin Spreckels. "An adaptive penalty function with meta-modeling for constrained problems". En 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557721.
Texto completoVilaça, Rita y Ana Maria A. C. Rocha. "An adaptive penalty method for DIRECT algorithm in engineering optimization". En NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756265.
Texto completoInformes sobre el tema "Adaptive Penalty"
Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, marzo de 2022. http://dx.doi.org/10.4271/2022-01-0616.
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