Bibliographies thématiques / GENETIC FRAMEWORK

Littérature scientifique sur le sujet « GENETIC FRAMEWORK »

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Publié le 11 septembre 2023

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Articles de revues sur le sujet "GENETIC FRAMEWORK"

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De Meester, Luc, Joost Vanoverbeke, Koen De Gelas, Raquel Ortells et Piet Spaak. « Genetic structure of cyclic parthenogenetic zooplankton populations – a conceptual framework ». Archiv für Hydrobiologie 167, no 1-4 (5 octobre 2006) : 217–44. http://dx.doi.org/10.1127/0003-9136/2006/0167-0217.

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Venkatraman, S., et G. G. Yen. « A Generic Framework for Constrained Optimization Using Genetic Algorithms ». IEEE Transactions on Evolutionary Computation 9, no 4 (août 2005) : 424–35. http://dx.doi.org/10.1109/tevc.2005.846817.

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Godfrey, K. « Genetic databank launches ethics framework ». BMJ 327, no 7417 (27 septembre 2003) : 700–0. http://dx.doi.org/10.1136/bmj.327.7417.700.

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Currens, Kenneth P., et Craig A. Busack. « A Framework for Assessing Genetic Vulnerability ». Fisheries 20, no 12 (décembre 1995) : 24–31. http://dx.doi.org/10.1577/1548-8446(1995)020<0024:affagv>2.0.co;2.

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Farashi, Sajjad, et Mohammad Mikaili. « Genetic Algorithm Framework for Spike Sorting ». International Journal of Image, Graphics and Signal Processing 7, no 4 (8 mars 2015) : 42–50. http://dx.doi.org/10.5815/ijigsp.2015.04.05.

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Parcy, François, Ove Nilsson, Maximilian A. Busch, Ilha Lee et Detlef Weigel. « A genetic framework for floral patterning ». Nature 395, no 6702 (octobre 1998) : 561–66. http://dx.doi.org/10.1038/26903.

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Terry, Sharon F. « An Evidence Framework for Genetic Testing ». Genetic Testing and Molecular Biomarkers 21, no 7 (juillet 2017) : 407–8. http://dx.doi.org/10.1089/gtmb.2017.29032.sjt.

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Vaidyanathan, Prashant, Bryan S. Der, Swapnil Bhatia, Nicholas Roehner, Ryan Silva, Christopher A. Voigt et Douglas Densmore. « A Framework for Genetic Logic Synthesis ». Proceedings of the IEEE 103, no 11 (novembre 2015) : 2196–207. http://dx.doi.org/10.1109/jproc.2015.2443832.

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Rodriguez-Villalon, A., B. Gujas, Y. H. Kang, A. S. Breda, P. Cattaneo, S. Depuydt et C. S. Hardtke. « Molecular genetic framework for protophloem formation ». Proceedings of the National Academy of Sciences 111, no 31 (21 juillet 2014) : 11551–56. http://dx.doi.org/10.1073/pnas.1407337111.

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Twigg, Stephen R. F., et Andrew O. M. Wilkie. « A Genetic-Pathophysiological Framework for Craniosynostosis ». American Journal of Human Genetics 97, no 3 (septembre 2015) : 359–77. http://dx.doi.org/10.1016/j.ajhg.2015.07.006.

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Thèses sur le sujet "GENETIC FRAMEWORK"

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Wååg, Håkan. « Development of a Framework for Genetic Algorithms ». Thesis, Jönköping University, JTH, Computer and Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-11537.

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Genetic algorithms is a method of optimization that can be used tosolve many different kinds of problems. This thesis focuses ondeveloping a framework for genetic algorithms that is capable ofsolving at least the two problems explored in the work. Otherproblems are supported by allowing user-made extensions.The purpose of this thesis is to explore the possibilities of geneticalgorithms for optimization problems and artificial intelligenceapplications.To test the framework two applications are developed that look attwo distinct problems, both of which aim at demonstrating differentparts. The first problem is the so called Travelling SalesmanProblem. The second problem is a kind of artificial life simulator,where two groups of creatures, designated predator and prey, aretrying to survive.The application for the Travelling Salesman Problem measures theperformance of the framework by solving such problems usingdifferent settings. The creature simulator on the other hand is apractical application of a different aspect of the framework, wherethe results are compared against predefined data. The purpose is tosee whether the framework can be used to create useful data forthe creatures.The work showed how important a detailed design is. When thework began on the demonstration applications, things were noticedthat needed changing inside the framework. This led to redesigningparts of the framework to support the missing details. A conclusionfrom this is that being more thorough in the planning, andconsidering the possible use cases could have helped avoid thissituation.The results from the simulations showed that the framework iscapable of solving the specified problems, but the performance isnot the best. The framework can be used to solve arbitrary problemsby user-created extensions quite easily.

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Dighe, Rahul. « Human pattern nesting strategies in a genetic algorithms framework ». Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36083.

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Berntsson, Lars Johan. « An adaptive framework for Internet-based distributed genetic algorithms ». Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16242/1/Johan_Berntsson_Thesis.pdf.

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Genetic Algorithms (GAs) are search algorithms inspired by genetics and natural selection, and have been used to solve difficult problems in many disciplines, including modelling, control systems and automation. GAs are generally able to find good solutions in reasonable time, however as they are applied to larger and harder problems they are very demanding in terms of computation time and memory. The Internet is the most powerful parallel and distributed computation environment in the world, and the idle cycles and memories of computers on the Internet have been increasingly recognized as a huge untapped source of computation power. By combining Internet computing and GAs, this dissertation provides a framework for Internet-based parallel and distributed GAs that gives scientists and engineers an easy and affordable way to solve hard real world problems. Developing parallel computation applications on the Internet is quite unlike developing applications in traditional parallel computation environments, such as multiprocessor systems and clusters. This is because the Internet is different in many respects, such as communication overhead, heterogeneity and volatility. To develop an Internet-based GA, we need to understand the implication of these differences. For this purpose, a convergence model for heterogenous and volatile networks is presented and used in experiments that study GA performance and robustness in Internet-like scenarios. The main outcome of this research is an Internet-based distributed GA framework called G2DGA. G2DGA is an island model distributed GA, which can provide support for big populations needed to solve many real world problems. G2DGA uses a novel hybrid peer-to-peer (P2P) design with island node activity coordinated by supervisor nodes that offer a global overview of the GA search state. Compared to client/server approaches, the P2P architecture improves scalability and fault tolerance by allowing direct communication between the islands and avoiding single-point-of-failure situations. One of the defining characteristics of Internet computing is the dynamics and volatility of the environment, and a parallel and distributed GA that does not adapt to its environment cannot use the available resources efficiently. Two novel adaptive methods are investigated. The first method is migration topology adaptation, which uses clustering on elite individuals from each island to rebuild the migration topology. Experiments with the migration topology adapter show that it gives G2DGA better performance than a GA with static migration topology of a similar or larger connectivity level. The second method is population size adaptation, which automatically finds the number of islands and island population sizes needed to solve a given problem efficiently. Experiments on the population size adapter show that it is robust, and compares favourably with the traditional trial-and-error approach in terms of computational effort and solution quality. The scalability and robustness of G2DGA has been extensively tested in network scenarios of varying volatility and heterogeneity. Experiments with up to 60 computers were conducted in computer laboratories, while more complex network scenarios have been studied in an Internet simulator. In the experiments, G2DGA consistently performs as well as, and usually significantly better than, static distributed GAs and the difference grows larger with increased network instability. The results show that G2DGA, by continuously adjusting the migration policy and the population size, can detect and make efficient use of idle cycles donated over volatile Internet connections. To demonstrate that G2DGA can be used to implement and solve real world problems, a challenging application in VLSI design was developed and used in the testing of the framework. The application is a multi-layer floorplanner, which uses a novel GA representation and operators based on a slicing structure approach. Its packing quality compares favourably with other multi-layer floorplanners found in the literature. Internet-based distributed GA research is exciting and important since it enables GAs to be applied to problem areas where resource limitations make traditional approaches unworkable. G2DGA provides a scalable and robust Internet-based distributed GA framework that can serve as a foundation for future work in the field.
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Berntsson, Lars Johan. « An adaptive framework for Internet-based distributed genetic algorithms ». Queensland University of Technology, 2006. http://eprints.qut.edu.au/16242/.

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Genetic Algorithms (GAs) are search algorithms inspired by genetics and natural selection, and have been used to solve difficult problems in many disciplines, including modelling, control systems and automation. GAs are generally able to find good solutions in reasonable time, however as they are applied to larger and harder problems they are very demanding in terms of computation time and memory. The Internet is the most powerful parallel and distributed computation environment in the world, and the idle cycles and memories of computers on the Internet have been increasingly recognized as a huge untapped source of computation power. By combining Internet computing and GAs, this dissertation provides a framework for Internet-based parallel and distributed GAs that gives scientists and engineers an easy and affordable way to solve hard real world problems. Developing parallel computation applications on the Internet is quite unlike developing applications in traditional parallel computation environments, such as multiprocessor systems and clusters. This is because the Internet is different in many respects, such as communication overhead, heterogeneity and volatility. To develop an Internet-based GA, we need to understand the implication of these differences. For this purpose, a convergence model for heterogenous and volatile networks is presented and used in experiments that study GA performance and robustness in Internet-like scenarios. The main outcome of this research is an Internet-based distributed GA framework called G2DGA. G2DGA is an island model distributed GA, which can provide support for big populations needed to solve many real world problems. G2DGA uses a novel hybrid peer-to-peer (P2P) design with island node activity coordinated by supervisor nodes that offer a global overview of the GA search state. Compared to client/server approaches, the P2P architecture improves scalability and fault tolerance by allowing direct communication between the islands and avoiding single-point-of-failure situations. One of the defining characteristics of Internet computing is the dynamics and volatility of the environment, and a parallel and distributed GA that does not adapt to its environment cannot use the available resources efficiently. Two novel adaptive methods are investigated. The first method is migration topology adaptation, which uses clustering on elite individuals from each island to rebuild the migration topology. Experiments with the migration topology adapter show that it gives G2DGA better performance than a GA with static migration topology of a similar or larger connectivity level. The second method is population size adaptation, which automatically finds the number of islands and island population sizes needed to solve a given problem efficiently. Experiments on the population size adapter show that it is robust, and compares favourably with the traditional trial-and-error approach in terms of computational effort and solution quality. The scalability and robustness of G2DGA has been extensively tested in network scenarios of varying volatility and heterogeneity. Experiments with up to 60 computers were conducted in computer laboratories, while more complex network scenarios have been studied in an Internet simulator. In the experiments, G2DGA consistently performs as well as, and usually significantly better than, static distributed GAs and the difference grows larger with increased network instability. The results show that G2DGA, by continuously adjusting the migration policy and the population size, can detect and make efficient use of idle cycles donated over volatile Internet connections. To demonstrate that G2DGA can be used to implement and solve real world problems, a challenging application in VLSI design was developed and used in the testing of the framework. The application is a multi-layer floorplanner, which uses a novel GA representation and operators based on a slicing structure approach. Its packing quality compares favourably with other multi-layer floorplanners found in the literature. Internet-based distributed GA research is exciting and important since it enables GAs to be applied to problem areas where resource limitations make traditional approaches unworkable. G2DGA provides a scalable and robust Internet-based distributed GA framework that can serve as a foundation for future work in the field.
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Song, Yeunjoo E. « New Score Tests for Genetic Linkage Analysis in a Likelihood Framework ». Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354561219.

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Kumuthini, Judit. « Extraction of genetic network from microarray data using Bayesian framework ». Thesis, Cranfield University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.442547.

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Mittra, James. « Genetic information, life assurance, and the UK policy and regulatory framework ». Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/106450/.

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This thesis provides the first extensive sociological analysis of the genetics and life assurance debate in the UK. It uses data from original qualitative interviews, as well as various policy documents and reports, to investigate the likely implications of genetic information for life assurance provision, reveal the narrative strategies used by key stakeholders as they account for their concerns on the issue, and evaluate the efficacy of the policy and regulatory framework. It also attempts to evaluate the suitability of the citizens’ jury model as an alternative to existing decision-making procedures. The thesis begins by revealing the most likely social, commercial, legal, and ethical implications of allowing insurers to access new kinds of genetic information. A history of insurance, risk and probability is used as a starting point to challenge many of the pervasive fears and anxieties. This part of the thesis critically analyses the social and philosophical basis of such contested notions as ’discrimination’, 'social exclusion’, 'genetic information', and ‘social justice’, and begins to reveal some of the key strategies of stakeholders in the debate. The thesis then analyses stakeholder accounts of their concerns, and begins to reveal the ways in which they draw on a broad narrative repertoire to give their beliefs a degree of moral legitimacy/coherency. The impact this may have on the quality of debate is also investigated. Following from the analysis of stakeholder accounts, the thesis proceeds to investigate the nature of the policymaking and regulatory framework. Through a sociological analysis of the work of various advisory committees, which led to the implementation of a moratorium on insurers' use of genetic information, the thesis investigates how fair and equitable the overall political process has been, particularly in terms of the treatment of stakeholder evidence. It also assesses the role of the public and media in shaping the political response to this issue. The thesis concludes by assessing the citizens’ jury as suitable procedures for resolving the conflicts around genetic information and life assurance. Both the potential advantages and persistent problems with the model are critically evaluated.
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Parandekar, Amey V. « Development of a Decision Support Framework forIntegrated Watershed Water Quality Management and a Generic Genetic Algorithm Based Optimizer ». NCSU, 1999. http://www.lib.ncsu.edu/theses/available/etd-19990822-032656.

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PARANDEKAR, AMEY, VIJAY. Development of a Decision Support Framework for Integrated Watershed Water Quality Management and a Generic Genetic Algorithm Based Optimizer. (Under the direction of Dr. S. Ranji Ranjithan.)The watershed management approach is a framework for addressing water quality problems at a watershed scale in an integrated manner that considers many conflicting issues including cost, environmental impact and equity in evaluating alternative control strategies. This framework enhances the capabilities of current environmental analysis frameworks by the inclusion of additional systems analytic tools such as optimization algorithms that enable efficient search for cost effective control strategies and uncertainty analysis procedures that estimate the reliability in achieving water quality targets. Traditional optimization procedures impose severe restrictions in using complex nonlinear environmental processes within a systematic search. Hence, genetic algorithms (GAs), a class of general, probabilistic, heuristic, global, search procedures, are used. Current implementation of this framework is coupled with US EPA's BASINS software system. A component of the current research is also the development of GA object classes and optimization model classes for generic use. A graphical user interface allows users to formulate mathematical programming problems and solve them using GA methodology. This set of GA object and the user interface classes together comprise the Generic Genetic Algorithm Based Optimizer (GeGAOpt), which is demonstrated through applications in solving interactively several unconstrained as well as constrained function optimization problems.Design of these systems is based on object oriented paradigm and current software engineering practices such as object oriented analysis (OOA) and object oriented design (OOD). The development follows the waterfall model for software development. The Unified Modeling Language (UML) is used for the design. The implementation is carried out using the JavaTM programming environment

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Silva, Carlos H. « A Proposed Framework for Establishing Optimal Genetic Designsfor Estimating Narrow-sense Heritability ». NCSU, 2000. http://www.lib.ncsu.edu/theses/available/etd-20000414-113213.

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We develop a framework for establishing sample sizes in breeding plans, so that one is able to estimate narrow-sense heritability with smallest possible variance, for a given amount of effort. We call this an optimal genetic design. The framework allows one to compare the variances of estimators of narrow-sense heritability, when estimated from each one of the alternative plans under consideration, and does not require data simulation, but does require computer programming. We apply it to the study of a peanut (Arachis hypogaea) breading example, in order to determine the ideal number of plants to be selected at each generation. We also propose a methodology that allows one to estimate the additive genetic variance for the estimation of the narrow-sense heritability using MINQUE and REML, without an analysis of variance model. It requires that one can build the matrix of genetic variances and covariances among the subjects on which observations are taken. This methodology can be easily adapted to ANOVA-based methods, and we exemplify by using Henderson's Method III. We compare Henderson's Method III, MINQUE, and REML under the proposed methodology, with an emphasis on comparing these estimation methods with non-normally distributed data and unbalanced designs. A location-scale transformation of the beta density is proposed for simulation of non-normal data.

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Aevan, Nadjib Danial. « MDO Framework for Design of Human PoweredPropellers using Multi-Objective Genetic Algorithm ». Thesis, Linköpings universitet, Fluida och mekatroniska system, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-123626.

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This thesis showcases the challenges, downsides and advantages to building a MultiDisciplinary Optimization (MDO) framework to automate the generation of an efficientpropeller design built for lightly loaded operation, more specifically for humanpowered aircrafts. Two years ago, a human powered aircraft project was initiatedat Linköping University. With the help of several courses, various students performedconceptional design, calculated and finally manufactured a propeller bymeans of various materials and manufacturing techniques. The performance ofthe current propeller is utilized for benchmarking and comparing results obtainedby the MDO process.The developed MDO framework is constructed as a modeFRONITER project wereseveral Computer Aided Engineering softwares (CAE) such as MATLAB, CATIAand XFOIL are connected to perform multiple consequent optimization subprocesses.The user is presented with several design constraints such as blade quantity,required input power, segment-wise airfoil thickness, desired lift coefficientetc. Also, 6 global search optimization algorithms are investigated to determinethe one which generate most efficient result according to several set standards.The optimization process is thereafter initialized by identifying the most efficientchord distribution with a help of an initial blade cross-section which has been previouslyused in other human powered propellers, the findings are thereafter usedto determine the flow conditions at different propeller stations. Two different aerodynamicoptimized shapes are generated with the help of consecutively performedsubprocesses. The optimized propeller requires 7.5 W less input power to generatenearly equivalent thrust as the original propeller with a total efficiency exceedingthe 90 % mark (90.25 %). Moreover, the MDO framework include an automationprocess to generate a CAD design of the optimized propeller. The generatedCAD file illustrates a individual surface blade decrease of 12.5 % compared tothe original design, the lightweight design and lower input power yield an overallpropulsion system which is less tedious to operate.
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Livres sur le sujet "GENETIC FRAMEWORK"

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Field testing genetically modified organisms : Framework for decisions. Washington, D.C : National Academy Press, 1989.

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Papua New Guinea. Dept. of Environment and Conservation. Papua New Guinea's national biosafety framework. National Capital District], P.N.G : Department of Environment and Conservation, 2005.

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Marco, Mazzoni Cosimo, dir. A legal framework for bioethics. The Hague : Kluwer Law International, 1998.

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Tan, Xiangtian. Framework for Mapping Gene Regulation via Single-cell Genetic Screens. [New York, N.Y.?] : [publisher not identified], 2021.

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Daniele, Manzella, Martyniuk Elzbieta et Food and Agriculture Organization of the United Nations. Legal Office., dir. The legal framework for the management of animal genetic resources. Rome : Food and Agriculture Organization of the United Nations, 2005.

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Papua New Guinea. Dept. of Environment and Conservation. Papua New Guinea's national biosafety framework. National Capital District], P.N.G : Department of Environment and Conservation, 2005.

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Developing the institutional framework for the management of animal genetic resources. Rome : Commission on Genetic Resources for Food and Agriculture, Food and Agriculture Organization of the United Nations, 2011.

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Scottish Health Service Advisory Council. National Advisory Committee for Scientific Services. The service application of molecular genetic technology : A framework for the future : a report. (Edinburgh) : Scottish Office Home and Health Department, 1994.

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National Science and Technology Council (U.S.). Interagency Working Group on Domestic Animal Genomics. Coordination of programs on domestic animal genomics : The federal framework : progress report. Washington, D.C : Executive Office of the President, National Science and Technology Council, 2004.

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Coordination of programs on domestic animal genomics : The federal framework : progress report. Washington, D.C : Executive Office of the President, National Science and Technology Council, 2004.

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Chapitres de livres sur le sujet "GENETIC FRAMEWORK"

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Cotillon, Alban, Philip Valencia et Raja Jurdak. « Android Genetic Programming Framework ». Dans Lecture Notes in Computer Science, 13–24. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29139-5_2.

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Virchow, Detlef. « Economic Framework of Conservation ». Dans Conservation of Genetic Resources, 45–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-58450-3_3.

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Folkman, Lukas, Wayne Pullan et Bela Stantic. « Generic Parallel Genetic Algorithm Framework for Protein Optimisation ». Dans Algorithms and Architectures for Parallel Processing, 64–73. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24669-2_7.

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Gorwood, Philip, Yann Le Strat et Nicolas Ramoz. « A Genetic Framework for Addiction ». Dans The Routledge Handbook of Philosophy and Science of Addiction, 275–85. 1 [edition]. | New York : Routledge, 2018. | Series : Routledge handbooks in philosophy : Routledge, 2018. http://dx.doi.org/10.4324/9781315689197-23.

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Owen, Tim. « Constructing a Genetic-Social Framework ». Dans Criminological Theory, 63–115. London : Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137316950_3.

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Branke, Jürgen, Michael Stein et Hartmut Schmeck. « A Unified Framework for Metaheuristics ». Dans Genetic and Evolutionary Computation — GECCO 2003, 1568–69. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45110-2_28.

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Maulik, Ujjwal, Sanghamitra Bandyopadhyay et Anirban Mukhopadhyay. « Clustering Categorical Data in a Multiobjective Framework ». Dans Multiobjective Genetic Algorithms for Clustering, 173–94. Berlin, Heidelberg : Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16615-0_8.

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Krawiec, Krzysztof. « The framework of behavioral program synthesis ». Dans Behavioral Program Synthesis with Genetic Programming, 35–41. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27565-9_3.

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Lardeux, Frédéric, et Adrien Goëffon. « A Dynamic Island-Based Genetic Algorithms Framework ». Dans Lecture Notes in Computer Science, 156–65. Berlin, Heidelberg : Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17298-4_16.

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Marino, Francesco, Giovanni Squillero et Alberto Tonda. « A General-Purpose Framework for Genetic Improvement ». Dans Parallel Problem Solving from Nature – PPSN XIV, 345–52. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45823-6_32.

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Actes de conférences sur le sujet "GENETIC FRAMEWORK"

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Li, Genghui, Qingfu Zhang et Weifeng Gao. « Multipopulation evolution framework for multifactorial optimization ». Dans GECCO '18 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2018. http://dx.doi.org/10.1145/3205651.3205761.

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Tamminedi, Tejaswi, Priya Ganapathy, Lei Zhang et Jacob Yadegar. « Classifier fusion framework using genetic algorithms ». Dans 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2011). IEEE, 2011. http://dx.doi.org/10.1109/pimrc.2011.6139912.

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Krishnan, Prajindra Sankar, Sieh Kiong Tiong et Johnny Koh. « Parallel distributed genetic algorithm development based on microcontrollers framework ». Dans DFmA 2008. 2008 1st International Conference on Distributed Framework & ; Applications. IEEE, 2008. http://dx.doi.org/10.1109/icdfma.2008.4784411.

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Nebro, Antonio J., Juan J. Durillo et Matthieu Vergne. « Redesigning the jMetal Multi-Objective Optimization Framework ». Dans GECCO '15 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2015. http://dx.doi.org/10.1145/2739482.2768462.

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Nebro, Antonio J., Cristóbal Barba-González, José García Nieto, José A. Cordero et José F. Aldana Montes. « Design and architecture of the jMetaISP framework ». Dans GECCO '17 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3067695.3082466.

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Leclerc, Guillaume, Joshua E. Auerbach, Giovanni Iacca et Dario Floreano. « The Seamless Peer and Cloud Evolution Framework ». Dans GECCO '16 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2016. http://dx.doi.org/10.1145/2908812.2908886.

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Liventsev, Vadim, Aki Härmä et Milan Petković. « Neurogenetic programming framework for explainable reinforcement learning ». Dans GECCO '21 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2021. http://dx.doi.org/10.1145/3449726.3459537.

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Guha, Ritam, Wei Ao, Stephen Kelly, Vishnu Boddeti, Erik Goodman, Wolfgang Banzhaf et Kalyanmoy Deb. « MOAZ : A Multi-Objective AutoML-Zero Framework ». Dans GECCO '23 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2023. http://dx.doi.org/10.1145/3583131.3590391.

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Beham, Andreas, Johannes Karder, Gabriel Kronberger, Stefan Wagner, Michael Kommenda et Andreas Scheibenpflug. « Scripting and framework integration in heuristic optimization environments ». Dans GECCO '14 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2014. http://dx.doi.org/10.1145/2598394.2605690.

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Kelly, Stephen, Jacob Newsted, Wolfgang Banzhaf et Cedric Gondro. « A modular memory framework for time series prediction ». Dans GECCO '20 : Genetic and Evolutionary Computation Conference. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3377930.3390216.

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Rapports d'organisations sur le sujet "GENETIC FRAMEWORK"

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Liepins, G. A framework for studying genetic optiization of complex systems. Office of Scientific and Technical Information (OSTI), septembre 1989. http://dx.doi.org/10.2172/5462548.

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Rintoul, Mark Daniel, Elebeoba Eni May, William Michael Brown, Anna Marie Johnston et Jean-Paul Watson. Deciphering the genetic regulatory code using an inverse error control coding framework. Office of Scientific and Technical Information (OSTI), mars 2005. http://dx.doi.org/10.2172/922758.

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Wenren, Yonghu, Joon Lim, Luke Allen, Robert Haehnel et Ian Dettwiler. Helicopter rotor blade planform optimization using parametric design and multi-objective genetic algorithm. Engineer Research and Development Center (U.S.), décembre 2022. http://dx.doi.org/10.21079/11681/46261.

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In this paper, an automated framework is presented to perform helicopter rotor blade planform optimization. This framework contains three elements, Dakota, ParBlade, and RCAS. These elements are integrated into an environment control tool, Galaxy Simulation Builder, which is used to carry out the optimization. The main objective of this work is to conduct rotor performance design optimizations for forward flight and hover. The blade design variables manipulated by ParBlade are twist, sweep, and anhedral. The multi-objective genetic algorithm method is used in this study to search for the optimum blade design; the optimization objective is to minimize the rotor power required. Following design parameter substitution, ParBlade generates the modified blade shape and updates the rotor blade properties in the RCAS script before running RCAS. After the RCAS simulations are complete, the desired performance metrics (objectives and constraints) are extracted and returned to the Dakota optimizer. Demonstrative optimization case studies were conducted using a UH-60A main rotor as the base case. Rotor power in hover and forward flight, at advance ratio 𝜇𝜇 = 0.3, are used as objective functions. The results of this study show improvement in rotor power of 6.13% and 8.52% in hover and an advance ratio of 0.3, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 12.42% reduction at 𝜇𝜇 = 0.4.
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Allen, J., et S. Velsko. A Statistical Framework for Microbial Source Attribution : Measuring Uncertainty in Host Transmission Events Inferred from Genetic Data (Part 2 of a 2 Part Report). Office of Scientific and Technical Information (OSTI), novembre 2009. http://dx.doi.org/10.2172/971053.

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Eshed-Williams, Leor, et Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, janvier 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide significant understanding of meristem maintenance and cell fate specification by studying four transcription factors acting at the meristem. Our original aim was to identify the direct target genes of WUS, STM, KNAT6 and CNA transcription factor in a genome wide scale and the manner by which they regulate their targets. Our goal was to integrate this data into a regulatory model of cell fate specification in the SAM and to identify key genes within the model for further study. We have generated transgenic plants carrying the four TF with two different tags and preformed chromatin Immunoprecipitation (ChIP) assay to identify the TF direct target genes. Due to unforeseen obstacles we have been delayed in achieving this aim but hope to accomplish it soon. Using the GR inducible system, genetic approach and transcriptome analysis [mRNA-seq] we provided a new look at meristem activity and its regulation of morphogenesis and phyllotaxy and propose a coherent framework for the role of many factors acting in meristem development and maintenance. We provided evidence for 3 different mechanisms for the regulation of WUS expression, DNA methylation, a second receptor pathway - the ERECTA receptor and the CNA TF that negatively regulates WUS expression in its own domain, the Organizing Center. We found that once the WUS expression level surpasses a certain threshold it alters cell identity at the periphery of the inflorescence meristem from floral meristem to carpel fate [FM]. When WUS expression highly elevated in the FM, the meristem turn into indeterminate. We showed that WUS activate cytokinine, inhibit auxin response and represses the genes required for root identity fate and that gradual increase in WUCHEL activity leads to gradual meristem enlargement that affect phyllotaxis. We also propose a model in which the direction of WUS domain expansion laterally or upward affects meristem structure differently. We preformed mRNA-seq on meristems with different size and structure followed by k-means clustering and identified groups of genes that are expressed in specific domains at the meristem. We will integrate this data with the ChIP-seq of the 4 TF to add another layer to the genetic network regulating meristem activity.
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de Miguel Beriain, Iñigo, Aliuska Duardo Sánchez et José Antonio Castillo Parrilla. What Can We Do with the Data of Deceased People ? A Normative Proposal. Universitätsbibliothek J. C. Senckenberg, Frankfurt am Main, 2021. http://dx.doi.org/10.21248/gups.64580.

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The health and genetic data of deceased people are a particularly important asset in the field of biomedical research. However, in practice, using them is compli- cated, as the legal framework that should regulate their use has not been fully developed yet. The General Data Protection Regulation (GDPR) is not applicable to such data and the Member States have not been able to agree on an alternative regulation. Recently, normative models have been proposed in an attempt to face this issue. The most well- known of these is posthumous medical data donation (PMDD). This proposal supports an opt-in donation system of health data for research purposes. In this article, we argue that PMDD is not a useful model for addressing the issue at hand, as it does not consider that some of these data (the genetic data) may be the personal data of the living relatives of the deceased. Furthermore, we find the reasons supporting an opt-in model less convincing than those that vouch for alternative systems. Indeed, we propose a normative framework that is based on the opt-out system for non-personal data combined with the application of the GDPR to the relatives’ personal data.
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Allen, Luke, Joon Lim, Robert Haehnel et Ian Dettwiller. Helicopter rotor blade multiple-section optimization with performance. Engineer Research and Development Center (U.S.), juin 2021. http://dx.doi.org/10.21079/11681/41031.

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This paper presents advancements in a surrogate-based, rotor blade design optimization framework for improved helicopter performance. The framework builds on previous successes by allowing multiple airfoil sections to designed simultaneously to minimize required rotor power in multiple flight conditions. Rotor power in hover and forward flight, at advance ratio 𝜇 = 0.3, are used as objective functions in a multi-objective genetic algorithm. The framework is constructed using Galaxy Simulation Builder with optimization provided through integration with Dakota. Three independent airfoil sections are morphed using ParFoil and aerodynamic coefficients for the updated airfoil shapes (i.e., lift, drag, moment) are calculated using linear interpolation from a database generated using C81Gen/ARC2D. Final rotor performance is then calculated using RCAS. Several demonstrative optimization case studies were conducted using the UH-60A main rotor. The degrees of freedom for this case are limited to the airfoil camber, camber crest position, thickness, and thickness crest position for each of the sections. The results of the three-segment case study show improvements in rotor power of 4.3% and 0.8% in forward flight and hover, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 6.0% reduction at 𝜇 = 0.35, and 8.8% reduction at 𝜇 = 0.4.
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Seale, Maria, Natàlia Garcia-Reyero, R. Salter et Alicia Ruvinsky. An epigenetic modeling approach for adaptive prognostics of engineered systems. Engineer Research and Development Center (U.S.), juillet 2021. http://dx.doi.org/10.21079/11681/41282.

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Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
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Appel, Gordon, et Todd Haverlock. Draft Infrastructure Framework for a Generic Repository Licensing Organization. Office of Scientific and Technical Information (OSTI), octobre 2015. http://dx.doi.org/10.2172/1762054.

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Appel, Gordon. Draft Infrastructure Framework for a Generic Repository Development Organization. Office of Scientific and Technical Information (OSTI), septembre 2016. http://dx.doi.org/10.2172/1762059.

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