Academic literature on the topic 'Adaptive Penalty'
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Journal articles on the topic "Adaptive Penalty"
Zhou, Zhenhua, and Haijun Wu. "Convergence and Quasi-Optimality of an Adaptive Multi-Penalty Discontinuous Galerkin Method." Numerical Mathematics: Theory, Methods and Applications 9, no. 1 (February 2016): 51–86. http://dx.doi.org/10.4208/nmtma.2015.m1412.
Full textLiu, Yufeng, Hao Helen Zhang, Cheolwoo Park, and Jeongyoun Ahn. "Support vector machines with adaptive penalty." Computational Statistics & Data Analysis 51, no. 12 (August 2007): 6380–94. http://dx.doi.org/10.1016/j.csda.2007.02.006.
Full textGosain, Anjana, and Kavita Sachdeva. "Handling Constraints Using Penalty Functions in Materialized View Selection." International Journal of Natural Computing Research 8, no. 2 (April 2019): 1–17. http://dx.doi.org/10.4018/ijncr.2019040101.
Full textLa Rivière, Patrick J., Junguo Bian, and 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.
Full textLambert-Lacroix, Sophie, and Laurent Zwald. "The adaptive BerHu penalty in robust regression." Journal of Nonparametric Statistics 28, no. 3 (June 13, 2016): 487–514. http://dx.doi.org/10.1080/10485252.2016.1190359.
Full textMarchetti, Yuliya, and Qing Zhou. "Solution path clustering with adaptive concave penalty." Electronic Journal of Statistics 8, no. 1 (2014): 1569–603. http://dx.doi.org/10.1214/14-ejs934.
Full textTowfic, Zaid J., and Ali H. Sayed. "Adaptive Penalty-Based Distributed Stochastic Convex Optimization." IEEE Transactions on Signal Processing 62, no. 15 (August 2014): 3924–38. http://dx.doi.org/10.1109/tsp.2014.2331615.
Full textYu, Xinghuo, and Baolin Wu. "An Adaptive Penalty Function Method for Constrained Optimization with Evolutionary Programming." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 2 (March 20, 2000): 164–70. http://dx.doi.org/10.20965/jaciii.2000.p0164.
Full textYEH, CHANG-CHING, KUEI-CHUNG CHANG, TIEN-FU CHEN, and CHINGWEI YEH. "ADAPTIVE PIPELINE VOLTAGE SCALING IN HIGH PERFORMANCE MICROPROCESSOR." Journal of Circuits, Systems and Computers 19, no. 08 (December 2010): 1817–34. http://dx.doi.org/10.1142/s0218126610007146.
Full textWang, Yuanxin. "An Adaptive Variational Mode Decomposition Technique with Differential Evolution Algorithm and Its Application Analysis." Shock and Vibration 2021 (November 11, 2021): 1–5. http://dx.doi.org/10.1155/2021/2030128.
Full textDissertations / Theses on the topic "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.
Full textSarkar, 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.
Full textSvensson, 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.
Full textI 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, and 葉國智. "Differential Evolution Algorithm with Adaptive Penalty for Constrained Continuous Global Optimization." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/78691247950621499584.
Full text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, and 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.
Full text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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Books on the topic "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.
Full textBook chapters on the topic "Adaptive Penalty"
Huerta-Amante, Daniel Ángel, and Hugo Terashima-Marín. "Adaptive Penalty Weights When Solving Congress Timetabling." In 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.
Full textBarbosa, Helio J. C., and Afonso C. C. Lemonge. "An Adaptive Penalty Scheme for Steady-State Genetic Algorithms." In 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.
Full textBoon, W. M., and J. M. Nordbotten. "An Adaptive Penalty Method for Inequality Constrained Minimization Problems." In 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.
Full textGao, Lei, and William F. Rosenberger. "Adaptive Bayesian Design with Penalty Based on Toxicity-Efficacy Response." In Contributions to Statistics, 91–98. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00218-7_11.
Full textBarbosa, Helio J. C., Afonso C. C. Lemonge, and Heder S. Bernardino. "A Critical Review of Adaptive Penalty Techniques in Evolutionary Computation." In Infosys Science Foundation Series, 1–27. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2184-5_1.
Full textKale, Ishaan R., and Anand J. Kulkarni. "Constraint Handling Using the Self-Adaptive Penalty Function (SAPF) Approach." In Constraint Handling in Cohort Intelligence Algorithm, 49–59. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003245193-4.
Full textRoss, Peter, and Emma Hart. "An adaptive mutation scheme for a penalty-based graph-colouring GA." In Lecture Notes in Computer Science, 795–802. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056921.
Full textYen, Gary G. "An Adaptive Penalty Function for Handling Constraint in Multi-objective Evolutionary Optimization." In 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.
Full textChenggang, Cui, Yang Xiaofei, and Gao Tingyu. "A Self-adaptive Interior Penalty Based Differential Evolution Algorithm for Constrained Optimization." In Lecture Notes in Computer Science, 309–18. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11897-0_37.
Full textChen, Wentao, and Fei Han. "An Improved Multi-objective Particle Swarm Optimization with Adaptive Penalty Value for Feature Selection." In Communications in Computer and Information Science, 649–61. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3425-6_51.
Full textConference papers on the topic "Adaptive Penalty"
Fathy, Mohammed, and Michael Rotkowitz. "Essential Matrix Estimation Using Adaptive Penalty Formulations." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.50.
Full textNunez-Martinez, Jose, and Josep Mangues-Bafalluy. "Distributed Lyapunov drift-plus-penalty routing for WiFi mesh networks with adaptive penalty weight." In 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.
Full textTakahama, Tetsuyuki, and Setsuko Sakai. "An Equivalent Penalty Coefficient Method: An Adaptive Penalty Approach for Population-Based Constrained Optimization." In 2019 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2019. http://dx.doi.org/10.1109/cec.2019.8790360.
Full textKronvall, Ted, Filip Elvander, Stefan Ingi Adalbjornsson, and Andreas Jakobsson. "An adaptive penalty approach to multi-pitch estimation." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362339.
Full textXinchao Li, Qianhua He, Yanxiong Li, Changbin Li, and Zhingfeng Wang. "An adaptive premium penalty ant colony optimization algorithm." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890509.
Full textWang, Shiyuan, Yunfei Zheng, and Shukai Duan. "Sparse Huber adaptive filter with correntropy induced metric penalty." In 2016 35th Chinese Control Conference (CCC). IEEE, 2016. http://dx.doi.org/10.1109/chicc.2016.7554125.
Full textAlaboudi, Dheyaa, and Ali Alkenani. "Sparse sliced inverse regression based on adaptive lasso penalty." In 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.
Full textDjebedjian, Berge, Ashraf Yaseen, and Magdy Abou Rayan. "A New Adaptive Penalty Method for Constrained Genetic Algorithm and Its Application to Water Distribution Systems." In 2006 International Pipeline Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/ipc2006-10235.
Full textKramer, Oliver, Uli Schlachter, and Valentin Spreckels. "An adaptive penalty function with meta-modeling for constrained problems." In 2013 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2013. http://dx.doi.org/10.1109/cec.2013.6557721.
Full textVilaça, Rita, and Ana Maria A. C. Rocha. "An adaptive penalty method for DIRECT algorithm in engineering optimization." In 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.
Full textReports on the topic "Adaptive Penalty"
Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0616.
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