Bibliografías temáticas / Genetic algorithms – Statistical methods

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Literatura académica sobre el tema "Genetic algorithms – Statistical methods"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 6 de febrero de 2022

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Artículos de revistas sobre el tema "Genetic algorithms – Statistical methods"

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Dharani Pragada, Venkata Aditya, Akanistha Banerjee, and Srinivasan Venkataraman. "OPTIMISATION OF NAVAL SHIP COMPARTMENT LAYOUT DESIGN USING GENETIC ALGORITHM." Proceedings of the Design Society 1 (July 27, 2021): 2339–48. http://dx.doi.org/10.1017/pds.2021.495.

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AbstractAn efficient general arrangement is a cornerstone of a good ship design. A big part of the whole general arrangement process is finding an optimized compartment layout. This task is especially tricky since the multiple needs are often conflicting, and it becomes a serious challenge for the ship designers. To aid the ship designers, improved and reliable statistical and computation methods have come to the fore. Genetic algorithms are one of the most widely used methods. Islier's algorithm for the multi-facility layout problem and an improved genetic algorithm for the ship layout design
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Salimi, Amir Hossein, Jafar Masoompour Samakosh, Ehsan Sharifi, Mohammad Reza Hassanvand, Amir Noori, and Hary von Rautenkranz. "Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data." Water 11, no. 8 (August 10, 2019): 1653. http://dx.doi.org/10.3390/w11081653.

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Precipitation as a key parameter in hydrometeorology and other water-related applications always needs precise methods for assessing and predicting precipitation data. In this study, an effort has been conducted to downscale and evaluate a satellite precipitation estimation (SPE) product using artificial neural networks (ANN), and to impose a residual correction method for five separate daily heavy precipitation events localized over northeast Austria. For the ANN model, a precipitation variable was the chosen output and the inputs were temperature, MODIS cloud optical, and microphysical varia
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Hatjimihail, A. T. "Genetic algorithms-based design and optimization of statistical quality-control procedures." Clinical Chemistry 39, no. 9 (September 1, 1993): 1972–78. http://dx.doi.org/10.1093/clinchem/39.9.1972.

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Abstract In general, one cannot use algebraic or enumerative methods to optimize a quality-control (QC) procedure for detecting the total allowable analytical error with a stated probability with the minimum probability for false rejection. Genetic algorithms (GAs) offer an alternative, as they do not require knowledge of the objective function to be optimized and can search through large parameter spaces quickly. To explore the application of GAs in statistical QC, I developed two interactive computer programs based on the deterministic crowding genetic algorithm. Given an analytical process,
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Cotfas, Daniel T., Petru A. Cotfas, Mihai P. Oproiu, and Paul A. Ostafe. "Analytical versus Metaheuristic Methods to Extract the Photovoltaic Cells and Panel Parameters." International Journal of Photoenergy 2021 (September 17, 2021): 1–17. http://dx.doi.org/10.1155/2021/3608138.

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The parameters of the photovoltaic cells and panels are very important to forecast the power generated. There are a lot of methods to extract the parameters using analytical, metaheuristic, and hybrid algorithms. The comparison between the widely used analytical method and some of the best metaheuristic algorithms from the algorithm families is made for datasets from the specialized literature, using the following statistical tests: absolute error, root mean square error, and the coefficient of determination. The equivalent circuit and mathematical model considered is the single diode model. T
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Tucker, Allan, Jason Crampton, and Stephen Swift. "RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms." Evolutionary Computation 13, no. 4 (December 2005): 477–99. http://dx.doi.org/10.1162/106365605774666903.

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There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the perfor
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Tarnaris, Konstantinos, Ioanna Preka, Dionisis Kandris, and Alex Alexandridis. "Coverage and k-Coverage Optimization in Wireless Sensor Networks Using Computational Intelligence Methods: A Comparative Study." Electronics 9, no. 4 (April 21, 2020): 675. http://dx.doi.org/10.3390/electronics9040675.

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The domain of wireless sensor networks is considered to be among the most significant scientific regions thanks to the numerous benefits that their usage provides. The optimization of the performance of wireless sensor networks in terms of area coverage is a critical issue for the successful operation of every wireless sensor network. This article pursues the maximization of area coverage and area k-coverage by using computational intelligence algorithms, i.e., a genetic algorithm and a particle swarm optimization algorithm. Their performance was evaluated via comparative simulation tests, mad
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Huang, Chien-Feng, Chi-Jen Hsu, Chi-Chung Chen, Bao Rong Chang, and Chen-An Li. "An Intelligent Model for Pairs Trading Using Genetic Algorithms." Computational Intelligence and Neuroscience 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/939606.

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Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results sho
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Aizawa, Akiko N., and Benjamin W. Wah. "Scheduling of Genetic Algorithms in a Noisy Environment." Evolutionary Computation 2, no. 2 (June 1994): 97–122. http://dx.doi.org/10.1162/evco.1994.2.2.97.

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In this paper, we develop new methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment. Such methods are related to the scheduling of resources for tests performed in genetic algorithms. Assuming that the population size is given, we address two problems related to the design of efficient scheduling algorithms specifically important in noisy environments. First, we study the durution-scheduling problem that is related to setting dynamically the duration of each generation. Second, we study the sample-allocation problem that entails the adaptive dete
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Kang, Jae Youn, Byung Ik Choi, Hak Joo Lee, Sang Rok Lee, Joo Sung Kim, and Kee Joo Kim. "Genetic Algorithm Application in Multiaxial Fatigue Criteria Computation." International Journal of Modern Physics B 17, no. 08n09 (April 10, 2003): 1678–83. http://dx.doi.org/10.1142/s0217979203019502.

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Both critical plane and stress invariant approaches are used to evaluate fatigue limit criteria of machine component subjected to non-proportional cyclic loading. Critical plane methods require finding the smallest circle enclosing all the tips of shear stress vectors acting on the critical plane. In stress invariant methods, the maximum amplitude of the second invariant of the stress deviator should be determined. In this paper, the previous algorithms for constructing the minimum circumscribed circle or hyper-sphere are briefly reviewed and the method using genetic algorithm is proposed.
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Marcek, Dusan. "Some statistical and CI models to predict chaotic high-frequency financial data." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6419–30. http://dx.doi.org/10.3233/jifs-189107.

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To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A com
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Tesis sobre el tema "Genetic algorithms – Statistical methods"

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Czarn, Andrew Simon Timothy. "Statistical exploratory analysis of genetic algorithms." University of Western Australia. School of Computer Science and Software Engineering, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0030.

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[Truncated abstract] Genetic algorithms (GAs) have been extensively used and studied in computer science, yet there is no generally accepted methodology for exploring which parameters significantly affect performance, whether there is any interaction between parameters and how performance varies with respect to changes in parameters. This thesis presents a rigorous yet practical statistical methodology for the exploratory study of GAs. This methodology addresses the issues of experimental design, blocking, power and response curve analysis. It details how statistical analysis may assist the in
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Shen, Rujun, and 沈汝君. "Mining optimal technical trading rules with genetic algorithms." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B47870011.

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In recent years technical trading rules are widely known by more and more people, not only the academics many investors also learn to apply them in financial markets. One approach of constructing technical trading rules is to use technical indicators, such as moving average(MA) and filter rules. These trading rules are widely used possibly because the technical indicators are simple to compute and can be programmed easily. An alternative approach of constructing technical trading rules is to rely on some chart patterns. However, the patterns and signals detected by these rules are oft
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Barreau, Thibaud. "Strategic optimization of a global bank capital management using statistical methods on open data." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273413.

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This project is about the optimization of the capital management of a French global bank. Capital management corresponds here to allocating the available capital to the different business units. In this project, I focus on the optimization of the allocation of the risk weighted assets (RWA) between some of the business units of the bank, as a representation of the allocated capital. Emphasis is put on the market and retail part of the bank and the first step was to be able to model the evolution of a business unit given an economic environment. The second one was about optimizing the distribut
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Larsen, Ross Allen Andrew. "Food Shelf Life: Estimation and Experimental Design." Diss., CLICK HERE for online access, 2006. http://contentdm.lib.byu.edu/ETD/image/etd1315.pdf.

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Herrington, Hira B. "A Heuristic Evolutionary Method for the Complementary Cell Suppression Problem." NSUWorks, 2015. http://nsuworks.nova.edu/gscis_etd/28.

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Cell suppression is a common method for disclosure avoidance used to protect sensitive information in two-dimensional tables where row and column totals are published along with non-sensitive data. In tables with only positive cell values, cell suppression has been demonstrated to be non-deterministic NP-hard. Therefore, finding more efficient methods for producing low-cost solutions is an area of active research. Genetic algorithms (GA) have shown to be effective in finding good solutions to the cell suppression problem. However, these methods have the shortcoming that they tend to produce a
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ZHANG, GE. "STATISTICAL METHODS IN GENETIC ASSOCIATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1196099744.

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Valenzuela-Del, Rio Jose Eugenio. "Bayesian adaptive sampling for discrete design alternatives in conceptual design." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50263.

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The number of technology alternatives has lately grown to satisfy the increasingly demanding goals in modern engineering. These technology alternatives are handled in the design process as either concepts or categorical design inputs. Additionally, designers desire to bring into early design more and more accurate, but also computationally burdensome, simulation tools to obtain better performing initial designs that are more valuable in subsequent design stages. It constrains the computational budget to optimize the design space. These two factors unveil the need of a conceptual design methodo
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Rogers, Alex. "Modelling genetic algorithms and evolving populations." Thesis, University of Southampton, 2000. https://eprints.soton.ac.uk/261289/.

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A formalism for modelling the dynamics of genetic algorithms using methods from statistical physics, originally due to Pr¨ugel-Bennett and Shapiro, is extended to ranking selection, a form of selection commonly used in the genetic algorithm community. The extension allows a reduction in the number of macroscopic variables required to model the mean behaviour of the genetic algorithm. This reduction allows a more qualitative understanding of the dynamics to be developed without sacrificing quantitative accuracy. The work is extended beyond modelling the dynamics of the genetic algorithm. A cari
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Shar, Nisar Ahmed. "Statistical methods for predicting genetic regulation." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/16729/.

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Transcriptional regulation of gene expression is essential for cellular differentiation and function, and defects in the process are associated with cancer. Transcription is regulated by the cis-acting regulatory regions and trans-acting regulatory elements. Transcription factors bind on enhancers and repressors and form complexes by interacting with each other to control the expression of the genes. Understanding the regulation of genes would help us to understand the biological system and can be helpful in identifying therapeutic targets for diseases such as cancer. The ENCODE project has ma
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Pittman, Jennifer L. "Adaptive splines and genetic algorithms for optimal statistical modeling." Adobe Acrobat reader required to view the full dissertation, 2000. http://www.etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-23/index.html.

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Libros sobre el tema "Genetic algorithms – Statistical methods"

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An introduction to genetic algorithms. Cambridge, Mass: MIT Press, 1996.

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Bayesian phylogenetics: Methods, algorithms, and applications. Boca Raton: CRC Press/Taylor & Francis, 2014.

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Mathematical and statistical methods for genetic analysis. 2nd ed. New York: Springer, 2002.

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Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5.

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Lange, Kenneth. Mathematical and Statistical Methods for Genetic Analysis. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5.

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Mathematical and statistical methods for genetic analysis. New York: Springer, 1997.

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Rattray, Lars Magnus. Modelling the dynamics of genetic algorithms using statistical mechanics. Manchester: University of Manchester, 1997.

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Genetic data analysis: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1990.

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Weir, B. S. Genetic data analysis II: Methods for discrete population genetic data. Sunderland, Mass: Sinauer Associates, 1996.

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Barone, Piero, Arnoldo Frigessi, and Mauro Piccioni, eds. Stochastic Models, Statistical Methods, and Algorithms in Image Analysis. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2920-9.

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Capítulos de libros sobre el tema "Genetic algorithms – Statistical methods"

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Lange, Kenneth. "Counting Methods and the EM Algorithm." In Mathematical and Statistical Methods for Genetic Analysis, 19–34. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_2.

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Lange, Kenneth. "Counting Methods and the EM Algorithm." In Mathematical and Statistical Methods for Genetic Analysis, 21–38. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21750-5_2.

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Chen, Yingrui, Mark Elliot, and Duncan Smith. "The Application of Genetic Algorithms to Data Synthesis: A Comparison of Three Crossover Methods." In Privacy in Statistical Databases, 160–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99771-1_11.

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Battaglia, Francesco, Domenico Cucina, and Manuel Rizzo. "Periodic Autoregressive Models with Multiple Structural Changes by Genetic Algorithms." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 107–10. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89824-7_19.

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Kuri-Morales, Angel Fernando, and Jesús Gutiérrez-García. "Penalty Function Methods for Constrained Optimization with Genetic Algorithms: A Statistical Analysis." In MICAI 2002: Advances in Artificial Intelligence, 108–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46016-0_12.

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Corazza, Marco, Giovanni Fasano, and Riccardo Gusso. "Portfolio selection with an alternative measure of risk: Computational performances of particle swarm optimization and genetic algorithms." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 123–30. Milano: Springer Milan, 2012. http://dx.doi.org/10.1007/978-88-470-2342-0_15.

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Ossowski, Andrzej, and Anna Święcicka. "Statistical Genetic Algorithms." In Advances in Intelligent and Soft Computing, 143–54. Heidelberg: Physica-Verlag HD, 2001. http://dx.doi.org/10.1007/978-3-7908-1813-0_13.

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Elston, Robert C., Jaya Satagopan, and Shuying Sun. "Statistical Genetic Terminology." In Methods in Molecular Biology, 1–9. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7274-6_1.

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Jeffers, John N. R. "Genetic Algorithms I." In Machine Learning Methods for Ecological Applications, 107–21. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5_4.

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Stockwell, David R. B. "Genetic Algorithms II." In Machine Learning Methods for Ecological Applications, 123–44. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5289-5_5.

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Actas de conferencias sobre el tema "Genetic algorithms – Statistical methods"

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Reeves, C. R. "Genetic algorithms and statistical methods: a comparison." In 1st International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA). IEE, 1995. http://dx.doi.org/10.1049/cp:19951038.

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Carata, Serban-Vasile, Veta Ghenescu, Marian Ghenescu, Mihai Chindea, and Roxana Mihaescu. "Salt and Pepper Noise Removal by Combining Genetic Algorithms - Neural Networks and Statistical Methods." In 2018 12th International Conference on Communications (COMM). IEEE, 2018. http://dx.doi.org/10.1109/iccomm.2018.8430175.

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Carata, Serban-Vasile, Veta Ghenescu, Marian Ghenescu, Mihai Chindea, and Roxana Mihaescu. "Salt and Pepper Noise Removal by Combining Genetic Algorithms - Neural Networks and Statistical Methods." In 2018 12th International Conference on Communications (COMM). IEEE, 2018. http://dx.doi.org/10.1109/iccomm.2018.8484807.

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Zeliff, Kayla, Walter Bennette, and Scott Ferguson. "Multi-Objective Composite Panel Optimization Using Machine Learning Classifiers and Genetic Algorithms." 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/detc2016-60125.

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Design spaces that consist of millions or billions of design combinations pose a challenge to current methods for identifying optimal solutions. Complex analyses can also lead to lengthy computation times that further challenge the effectiveness of an algorithm in terms of solution quality and run-time. This work explores combining the design space exploration approach of a Multi-Objective Genetic Algorithm with different instance-based, statistical, rule-based and ensemble classifiers to reduce the number of unnecessary function evaluations associated with poorly performing designs. Results i
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Naranjo-Pérez, Javier, Andrés Sáez, Javier F. Jiménez-Alonso, Pablo Pachón, and Víctor Compán. "A Hybrid UKF-MAG Algorithm for Finite Element Model Updating of Historical Constructions." In IABSE Symposium, Guimarães 2019: Towards a Resilient Built Environment Risk and Asset Management. Zurich, Switzerland: International Association for Bridge and Structural Engineering (IABSE), 2019. http://dx.doi.org/10.2749/guimaraes.2019.0029.

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<p>The finite element model (FE) updating is a calibration method that allows minimizing the discrepancies between the numerical and experimental modal parameters. As result, a more accurate FE model is obtained and the structural analysis can represent the real behaviour of the structure. However, it is a high computational cost process. To overcome this issue, alternative techniques have been developed. This study focuses on the use of the unscented Kalman filter (UKF), which is a local optimization algorithm based on statistical estimation of parameters taken into account the measurem
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Serghiuta, Dumitru, John Tholammakkil, Naj Hammouda, and Anthony O’Hagan. "Testing of Statistical Procedures for Use in Optimization of Reactor Performance Under Aged Conditions." In 2014 22nd International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/icone22-31054.

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This paper discusses a framework for designing artificial test problems, evaluation criteria, and two of the benchmark tests developed under a research project initiated by the Canadian Nuclear Safety Commission to investigate the approaches for qualification of tolerance limit methods and algorithms proposed for application in optimization of CANDU reactor protection trip setpoints for aged conditions. A significant component of this investigation has been the development of a series of benchmark problems of gradually increased complexity, from simple “theoretical” problems up to complex prob
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Bordoloi, D. J., and Rajiv Tiwari. "Health Monitoring of Gear Elements Based on Time-Frequency Vibration by Support Vector Machine Algorithms." In ASME 2013 Gas Turbine India Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/gtindia2013-3772.

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Health monitoring of a gear box has been attempted by the support vector machine (SVM) learning technique with the help of time-frequency (wavelet) vibration data. Multi-fault classification capability of the SVM is suitably demonstrated that is based on the selection of SVM parameters. Different optimization methods (i.e., the grid-search method (GSM), the genetic algorithm (GA) and the artificial bee colony algorithm (ABCA)) have been performed for optimizing the SVM parameters. Four fault conditions have been considered including the no defect case. Time domain vibration signals were obtain
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Marri, Kiran, and Ramakrishnan Swaminathan. "Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9828.

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The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature.
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Ali, Muhammad Ansab, Tariq S. Khan, Saqib Salam, and Ebrahim Al Hajri. "Shape Optimization of Microchannels Using Surrogate Modelling." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-87780.

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To minimize the computational and optimization time, a numerical simulation of 3D microchannel heat sink was performed using surrogate model to achieve the optimum shape. Latin hypercube sampling method was used to explore the design space and to construct the model. The accuracy of the model was evaluated using statistical methods like coefficient of multiple determinations and root mean square error. Thermal resistance and pressure drop being conflicting objective functions were selected to optimize the geometric parameters of the microchannel. Multi objective shape optimization of design wa
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Ismail, Zuhaimy, Mohd Zulariffin Md Maarof, and Mohammad Fadzli. "Alteration of Box-Jenkins methodology by implementing genetic algorithm method." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907522.

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Informes sobre el tema "Genetic algorithms – Statistical methods"

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Gurdal, Zafer, Raphael T. Haftka, and Layne T. Watson. Wing Structural Design by Genetic Algorithms and Homotopy Methods. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada387245.

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Ott, Jurg. Statistical Genetic Methods for Localizing Multiple Breast Cancer Genes. Fort Belvoir, VA: Defense Technical Information Center, September 1995. http://dx.doi.org/10.21236/ada301699.

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Bazarov, Ivan, Matthew Andorf, William Bergan, Cameron Duncan, Vardan Khachatryan, Danilo Liarte, David Rubin, and James Sethna. Innovations in optimization and control of accelerators using methods of differential geometry and genetic algorithms. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1530158.

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Hutchinson, M. L., J. E. L. Corry, and R. H. Madden. A review of the impact of food processing on antimicrobial-resistant bacteria in secondary processed meats and meat products. Food Standards Agency, October 2020. http://dx.doi.org/10.46756/sci.fsa.bxn990.

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For meat and meat products, secondary processes are those that relate to the downstream of the primary chilling of carcasses. Secondary processes include maturation chilling, deboning, portioning, mincing and other operations such as thermal processing (cooking) that create fresh meat, meat preparations and ready-to-eat meat products. This review systematically identified and summarised information relating to antimicrobial resistance (AMR) during the manufacture of secondary processed meatand meat products (SPMMP). Systematic searching of eight literature databases was undertaken and the resu
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