Relevant bibliographies by topics / Population genetic models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Population genetic models'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Population genetic models"

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Pearce, G. P., and H. G. Spencer. "Population genetic models of genomic imprinting." Genetics 130, no. 4 (April 1, 1992): 899–907. http://dx.doi.org/10.1093/genetics/130.4.899.

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Abstract The phenomenon of genomic imprinting has recently excited much interest among experimental biologists. The population genetic consequences of imprinting, however, have remained largely unexplored. Several population genetic models are presented and the following conclusions drawn: (i) systems with genomic imprinting need not behave similarly to otherwise identical systems without imprinting; (ii) nevertheless, many of the models investigated can be shown to be formally equivalent to models without imprinting; (iii) consequently, imprinting often cannot be discovered by following allele frequency changes or examining equilibrium values; (iv) the formal equivalences fail to preserve some well known properties. For example, for populations incorporating genomic imprinting, parameter values exist that cause these populations to behave like populations without imprinting, but with heterozygote advantage, even though no such advantage is present in these imprinting populations. We call this last phenomenon "pseudoheterosis." The imprinting systems that fail to be formally equivalent to nonimprinting systems are those in which males and females are not equivalent, i.e., two-sex viability systems and sex-chromosome inactivation.
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Innan, Hideki. "Population genetic models of duplicated genes." Genetica 137, no. 1 (March 6, 2009): 19–37. http://dx.doi.org/10.1007/s10709-009-9355-1.

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Nagylaki, Thomas. "Multinomial-Sampling Models for Random Genetic Drift." Genetics 145, no. 2 (February 1, 1997): 485–91. http://dx.doi.org/10.1093/genetics/145.2.485.

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Three different derivations of models with multinomial sampling of genotypes in a finite population are presented. The three derivations correspond to the operation of random drift through population regulation, conditioning on the total number of progeny, and culling, respectively. Generations are discrete and nonoverlapping; the diploid population mates at random. Each derivation applies to a single multiallelic locus in a monoecious or dioecious population; in the latter case, the locus may be autosomal or X-linked. Mutation and viability selection are arbitrary; there are no fertility differences. In a monoecious population, the model yields the Wright-Fisher model (i.e., multinomial sampling of genes) if and only if the viabilities are multiplicative. In a dioecious population, the analogous reduction does not occur even for pure random drift. Thus, multinomial sampling of genotypes generally does not lead to multinomial sampling of genes. Although the Wright-Fisher model probably lacks a sound biological basis and may be inaccurate for small populations, it is usually (perhaps always) a good approximation for genotypic multinomial sampling in large populations.
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Fearnhead, Paul. "Perfect Simulation From Nonneutral Population Genetic Models: Variable Population Size and Population Subdivision." Genetics 174, no. 3 (September 1, 2006): 1397–406. http://dx.doi.org/10.1534/genetics.106.060681.

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Gaggiotti, Oscar E. "Population Genetic Models of Source–Sink Metapopulations." Theoretical Population Biology 50, no. 2 (October 1996): 178–208. http://dx.doi.org/10.1006/tpbi.1996.0028.

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Frisman, E. Ya, O. L. Zhdanova, and G. P. Neverova. "Ecological and Genetic Models in Population Biophysics." Biophysics 65, no. 5 (September 2020): 810–25. http://dx.doi.org/10.1134/s0006350920050061.

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Huttley, G. A., and S. R. Wilson. "Testing for Concordant Equilibria Between Population Samples." Genetics 156, no. 4 (December 1, 2000): 2127–35. http://dx.doi.org/10.1093/genetics/156.4.2127.

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Abstract A substantial body of theory has been developed to assess the effect of evolutionary forces on the distribution of genotypes, both single and multilocus, within populations. One area where the potential for application of this theory has not been fully appreciated concerns the extent to which population samples differ. Within populations, the divergence of genotype or haplotype frequencies from that expected under Hardy-Weinberg (HW) or linkage equilibrium can be measured as disequilibria coefficients. To assess population samples for concordant equilibria, an analytical framework for comparing disequilibria coefficients between populations is necessary. Here we present log-linear models to evaluate such hypotheses. These models have broad utility ranging from conventional population genetics to genetic epidemiology. We demonstrate the use of these log-linear models (1) as a test for genetic association with disease and (2) as a test for different levels of linkage disequilibria between human populations.
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Kelly, S. Thomas, and Hamish G. Spencer. "Population-genetic models of sex-limited genomic imprinting." Theoretical Population Biology 115 (June 2017): 35–44. http://dx.doi.org/10.1016/j.tpb.2017.03.004.

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Day, Troy, and Sylvain Gandon. "Applying population-genetic models in theoretical evolutionary epidemiology." Ecology Letters 10, no. 10 (October 2007): 876–88. http://dx.doi.org/10.1111/j.1461-0248.2007.01091.x.

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Fearnhead, Paul. "Perfect Simulation from Population Genetic Models with Selection." Theoretical Population Biology 59, no. 4 (June 2001): 263–79. http://dx.doi.org/10.1006/tpbi.2001.1514.

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Dissertations / Theses on the topic "Population genetic models"

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Herbots, Hilde Maria Jozefa Dominiek. "Stochastic models in population genetics : genealogy and genetic differentiation in structured populations." Thesis, Queen Mary, University of London, 1994. http://qmro.qmul.ac.uk/xmlui/handle/123456789/1482.

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The theory of probability and stochastic processes is applied to a current issue in population genetics, namely that of genealogy and genetic differentiation in subdivided populations. It is proved that under a reasonable model for reproduction and migration, the ancestral process of a sample from a subdivided population converges weakly, as the subpopulation sizes tend to infinity, to a continuous-time Markov chain called the "structured coalescent". The moment-generating function, the mean and the cond moment of the time since the most recent common ancestor (called the "coalescence time") of a pair of genes are calculated explicitly for a range of models of population structure. The value of Wright's coefficient FST, which serves as a measure of the subpopulation differentiation and which can be related to the coalescence times of pairs of genes sampled within or among subpopulations, is calculated explicitly for various models of population structure. It is shown that the dependence of FST on the mutation rate may be more marked than is generally believed, particularly when gene flow is restricted to an essentially one-dimensional habitat with a large number of subpopulations. Several more general results about genealogy and subpopulation differentiation are proved. Simple relationships are found between moments of within and between population coalescence times. Weighting each subpopulation by its relative size, the asymptotic behaviour of FST at large mutation rates is independent of the details of population structure. Two sets of symmetry conditions on the population structure are found for which the mean coalescence time of a pair of genes from a single subpopulation is independent of the migration rate and equal to that of two individuals from a panmictic population of the same total size. Under graph-theoretic conditions on the population structure, there is a uniform relationship between the FST value of a pair of neighbouring subpopulations, in the limit of zero mutation rate, and the migration rate
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Sester-Huss, Elisabeth Mariko [Verfasser], and Peter [Akademischer Betreuer] Pfaffelhuber. "Population genetic models with selection, fluctuating environments and population structure." Freiburg : Universität, 2020. http://d-nb.info/1206095830/34.

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Newman, Randa E. "Hypertrophic Cardiomyopathy Genotype Prediction Models in a Pediatric Population." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459529335.

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Anderson, Eric C. "Monte Carlo methods for inference in population genetic models /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6368.

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Wong, Hor Yan. "Some theoretical aspects of self-incompatibility systems in plants." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249167.

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Camp, Nicola J. "Methods for genealogical analysis, with particular reference to Type II diabetes in the Pima." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364310.

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Gayley, Todd Warwick. "Genetic models of two-phenotype frequency-dependent selection." Diss., The University of Arizona, 1989. http://hdl.handle.net/10150/184883.

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The aim of this study is to place a wide variety of two-phenotype frequency-dependent selection models into a unified population-genetic framework. This work is used to illuminate the possible genetic constraints that may exist in such models, and to address the question of evolutionary modification of these constraints. The first part of Chapter 1 synthesizes from the literature a general framework for applying a genetic structure to a simple class of two-phenotype models. It shows that genetic constraints may prevent the population from achieving a predicted phenotypic equilibrium, but the population will equilibrate at a point that is as close as possible to the phenotypic equilibrium. The second part of Chapter 1 goes on to ask whether evolutionary modification of the genetic system might be expected to remove these constraints. Chapter 2 provides an example of the application of the framework developed in Chapter 1. It presents re-analysis of a model for the evolution of social behavior by reciprocation (Brown et al. 1982). The genetic results of Chapter 1 apply to this model without modification. I show that Brown et al. were unnecessarily restrictive in their assumptions about the types of genetic systems that support their conclusions. Chapter 3 discusses some models for the evolution of altruism that do not fit the assumptions of Chapter 1, despite their two-phenotype structure. These models violate the fundamental assumption of Chapter 1, this being the way in which individual fitness is derived from the behavioral fitnesses. The first part is a complete, in-depth analysis of diploid sib-sib kin selection. I show that some results from the basic model can be used, provided the behavioral inclusive fitness functions are substituted for the true behavioral fitnesses. The second part is an analysis of the validity of the concept of behavioral structure, as introduced by Michod and Sanderson (1985). I show that this concept is flawed as a general principle. Chapter 4 extends the basic model to the case of sex-allocation evolution. I show how many of the central results of sex-allocation theory can be derived more simply using a two-phenotype framework.
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Chapman, Nicola H. "Genome descent in isolated populations /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/9583.

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Reyes-Centeno, Hugo [Verfasser], and Katerina [Akademischer Betreuer] Harvati. "Quantitative and population genetic approaches for testing modern human out-of-Africa models / Hugo Reyes-Centeno ; Betreuer: Katerina Harvati." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164018361/34.

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Kool, Johnathan. "Connectivity and Genetic Structure in Coral Reef Ecosystems: Modeling and Analysis." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_dissertations/157.

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This dissertation examines aspects of the relationship between connectivity and the development of genetic structure in subdivided coral reef populations using both simulation and algebraic methods. The first chapter develops an object-oriented, individual based method of simulating the dynamics of genes in subdivided populations. The model is then used to investigate how changes to different components of population structure (e.g., connectivity, birth rate, population size) influence genetic structure through the use of autocorrelation analysis. The autocorrelograms also demonstrate how relationships between populations change at different spatial and temporal scales. The second chapter uses discrete multivariate distributions to model the relationship between connectivity, selection and resource use in subdivided populations. The equations provide a stochastic basis for multiple-niche polymorphism through differential resource use, and the role of scale in changing selective weightings is also considered. The third chapter uses matrix equations to study the expected development of genetic structure among Caribbean coral reefs. The results show an expected break between eastern and western portions of the Caribbean, as well as additional nested structure within the Bahamas, the central Caribbean (Jamaica and the reefs of the Nicaraguan Rise) and the Mesoamerican Barrier Reef. The matrix equations provide an efficient means of modeling the development of genetic structure in subdivided populations through time. The fourth chapter uses matrix equations to examine the expected development of genetic structure among Southeast Asian coral reefs. Projecting genetic structure reveals an expected unidirectional connection from the South China Sea into the Coral Triangle region via the Sulu Sea. Larvae appear to be restricted from moving back into the South China Sea by a cyclonic gyre in the Sulu Sea. Additional structure is also evident, including distinct clusters within the Philippines, in the vicinity of the Makassar Strait, in the Flores Sea, and near Halmahera and the Banda Sea. The ability to evaluate the expected development of genetic structure over time in subdivided populations offers a number of potential benefits, including the ability to ascertain the expected direction of gene flow, to delineate natural regions of exchange through clustering, or to identify critical areas for conservation or for managing the spread of invasive material via elasticity analysis.
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Books on the topic "Population genetic models"

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Korolʹ, A. B. Recombination variability and evolution: Algorithms of estimation and population-genetic models. London: Chapman & Hall, 1994.

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Theoretical population genetics. London: Unwin Hyman, 1990.

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Gale, J. S. Theoretical population genetics. Boston: Unwin Hyman, 1990.

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H, Schierup Mikkel, and Wiuf Carsten, eds. Gene genealogies, variation and evolution: A primer in coalescent theory. Oxford: Oxford University Press, 2005.

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Pedigree analysis in human genetics. Baltimore: Johns Hopkins University Press, 1986.

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Evolutionary genetics. Oxford: Oxford University Press, 1989.

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Evolutionary genetics. 2nd ed. Oxford: Oxford University Press, 1998.

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Fitness landscapes and the origin of species. Princeton, N.J: Princeton University Press, 2004.

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Population genetics of multiple loci. Chichester: Wiley, 2000.

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Etheridge, Alison. Some Mathematical Models from Population Genetics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16632-7.

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Book chapters on the topic "Population genetic models"

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Blath, Jochen, and Noemi Kurt. "Population genetic models of dormancy." In Probabilistic Structures in Evolution, 247–66. Zuerich, Switzerland: European Mathematical Society Publishing House, 2021. http://dx.doi.org/10.4171/ecr/17-1/12.

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Hoekstra, R. F. "Theory of Phenotypic Evolution: Genetic or Non-Genetic Models?" In Population Genetics and Evolution, 33–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73069-6_4.

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Gabriel, W. "Quantitative Genetic Models for Parthenogenetic Species." In Population Genetics and Evolution, 73–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73069-6_8.

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Etheridge, Alison. "Mutation and Random Genetic Drift." In Some Mathematical Models from Population Genetics, 5–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16632-7_2.

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Bürger, R. "The Maintenance of Genetic Variation: A Functional Analytic Approach to Quantitative Genetic Models." In Population Genetics and Evolution, 63–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73069-6_7.

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Walsh, Bruce. "Population - and Quantitative-Genetic Models of Selection Limits." In Plant Breeding Reviews, 177–225. Oxford, UK: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470650240.ch9.

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Montiel, Oscar, J. M. Cornejo, Carlos Sepúlveda, and Roberto Sepúlveda. "Obtaining Pharmacokinetic Population Models Using a Genetic Algorithm Approach." In Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, 305–17. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17747-2_24.

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Hartmann, Peter. "PSA Approach to Population Models for Parallel Genetic Algorithms." In Lecture Notes in Computer Science, 60–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48387-x_7.

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Walsh, Bruce. "Population-genetic models of the fates of duplicate genes." In Contemporary Issues in Genetics and Evolution, 279–94. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0229-5_16.

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Quicke, Donald L. J., Buntika A. Butcher, and Rachel A. Kruft Welton. "Population modelling including spatially explicit models." In Practical R for biologists: an introduction, 303–21. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0303.

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Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. R can be used for some quite fast modelling jobs but its speed is nowhere near that of a compiled programming language such as C++. This chapter shows how user-defined functions can be used to perform highly repetitive jobs efficiently, and demonstrates various mathematical functions. The first example shows how a vector can be incremented and the calculated points plotted on a graph as the simulation proceeds. The second example runs a loop, and each time passes values to a user-defined function, and receives back multiple values from that function, which it then stores for plotting later. The third example is necessarily more complex and shows how R code can be used to carry out spatially explicit analyses. Finally, a simple example shows how R can be used to teach how evolution takes place, even in the absence of natural selection due to genetic drift and population bottle-necking.
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Conference papers on the topic "Population genetic models"

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Monzón, Hugo, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, and Kiyoshi Tanaka. "Studying com partmental models interpolation to estimate MOEAs population size." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3321985.

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Monzón, Hugo, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, and Kiyoshi Tanaka. "Dynamic compartmental models for algorithm analysis and population size estimation." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3326912.

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"A MODIFIED MULTI-POPULATION GENETIC ALGORITHM FOR PARAMETER IDENTIFICATION OF CULTIVATION PROCESS MODELS." In International Conference on Evolutionary Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003080603480351.

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Sato, Koma, Yuta Tamura, and Kiyohito Tani. "Multi-Objective Optimization for Francis Turbine Runner Using Genetic Algorithm." In ASME 2014 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/imece2014-36401.

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A multi-objective optimization system for a Francis turbine runner that uses a genetic algorithm was developed. Francis turbine is widely used because of its flexibility of meriodinal geometry. However, since a runner of a Francis turbine consists of fixed blades, it can show a high performance in a narrower range of operating conditions compared with diagonal and axial hydraulic turbines. Thus, the design of a Francis turbine runner needs plentiful design experience. The aim of this study is to develop an automatic design system for a Francis turbine runner and evaluate its availability. This system optimizes six objective functions: runner efficiency, swirling losses at partial load point and over load point, minimum pressure coefficient on the blade surface, deviation of runner head, and area deviation of outlet opening from the baseline design. The runner head deviation represents the difference from the required specifications. The optimization procedures are as follows; first, an initial design population to create runner geometries was generated by Latin hypercube sampling method. Next, runner geometries were created in accordance with the design variables of the initial design population. Then, objective functions were evaluated. Since the area deviation of outlet opening has a strong correlation with the runner head deviation and can be calculated without computational fluid dynamics (CFD) analysis, we tried to reduce the number of CFD evaluations by using this correlation. For all runner geometries, the area deviation of outlet opening was calculated, and mesh generations and CFD analysis of those that exceeded a deviation threshold were skipped. The rest of the objective functions were evaluated by CFD analysis. Analysis models at design point, partial load point, and over load point were created. In the CFD stage, steady and single-phase analysis was executed to evaluate objective functions. Finally, the population of the next generation was created by using a multi-objective genetic algorithm. By iterating these procedures, optimized runners superior to the baseline design were obtained efficiently and automatically.
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Higgs, Paul G. "Linking population genetics to phylogenetics." In Stochastic Models in Biological Sciences. Warsaw: Institute of Mathematics Polish Academy of Sciences, 2008. http://dx.doi.org/10.4064/bc80-0-8.

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Canyurt, Olcay Ersel, and Harun Kemal O¨ztu¨rk. "Application of Genetic Algorithm (GA) Technique on Demand Estimation of Fossil Fuels in Turkey." In ASME 2007 Energy Sustainability Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/es2007-36260.

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The main objective of the present study is to investigate Turkey’s fossil fuels demand, projection and supplies by giving the structure of the Turkish industry and Turkish economic conditions. This present study develops several scenarios to analyze fossil fuels; such as, coal, oil and natural gas consumption and make future projections based on Genetic Algorithm (GA) notion, and examines the effect of the design parameters on the fossil fuels utilization values. The models developed in the nonlinear form are applied to the coal, oil and natural gas demand of Turkey. Several Genetic Algorithm Demand Estimation Models (GA-DEM) are developed to estimate the future coal, oil and natural gas demand values based on population, Gross National Product (GNP), import, export figures. It may be concluded that the proposed models can be used as an alternative solution and estimation techniques for the future fossil fuel utilization values of any country. Oil is the most important fuel in Turkey, contributing 43% of the Total Primary Energy Supply (TPES), followed by coal (almost 30% of TPES) and natural gas (11.8%). In the study, coil, oil and natural gas consumption of Turkey are projected. Estimation shows that the coal, oil and natural gas consumption values may increase 2.82, 1.73 and 4.83 times from 2000 to 2020.
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Chaari, Majdi, Jalel Ben Hmida, Abdennour C. Seibi, and Afef Fekih. "Steady-State Pressure Drop for Two-Phase Flow in Pipelines: An Integrated Genetic Algorithm-Artificial Neural Network Approach." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71854.

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This paper develops an artificial neural network (ANN) model for steady-state two-phase flow pressure drop estimation in pipelines. Mechanistic models are traditionally considered in pipeline flow modeling. However, their reliance on fundamental laws of physics can negatively impact their accuracy when dealing with large experimental data sets and various pipeline inclinations. Hence, ANN models prove to be highly accurate compared to mechanistic models. Dimensional analysis is used to derive a broad reservoir of dimensionless groups and form candidate inputs to the ANN model. Identifying the groups leading to the best correlation of the output variable requires a laborious and nonsystematic trial-and-error procedure. To circumvent this problem, genetic algorithms (GA) were considered to identify the best ANN input combination, thereby allowing a good prediction of steady-state two-phase flow pressure drop in pipelines with all inclinations. The sensitivity of the model accuracy to some GA parameters such as the population size and the parent selection scheme was investigated. The proof of concept of the proposed approach was illustrated using the Stanford multiphase flow database. Based on the obtained results, the proposed model was shown to outperform existing mechanistic models when cross-examined using the same database. In addition, the proposed model allowed good prediction accuracy for all pipe inclinations and all flow patterns.
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Xiao, Jie, and Bohdan Kulakowski. "Hybrid Genetic Algorithm: A Robust Parameter Estimation Technique and Its Application to Heavy Duty Vehicles." In ASME 2003 International Mechanical Engineering Congress and Exposition. ASMEDC, 2003. http://dx.doi.org/10.1115/imece2003-41934.

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This study aims at establishing an accurate yet efficient parameter estimation strategy for developing dynamic vehicle models that can be easily implemented for simulation and controller design purposes. Generally, conventional techniques such as Least Square Estimation (LSE), Maximum Likelihood Estimation (MLE), and Instrumental Variable Methods (IVM), can deliver sufficient estimation results for given models that are linear-in-the-parameter. However, many identification problems in the engineering world are very complex in nature and are quite difficult to solve by those techniques. For the nonlinear-in-the-parameter models, it is almost impossible to find an analytical solution. As a result, numerical algorithms have to be used in calculating the estimates. In the area of model parameter estimation for motor vehicles, most studies performed so far have been limited either to the linear-in-the-parameter models, or in their ability to handle multi-modal error surfaces. For models with nondifferentiable cost functions, the conventional methods will not be able to locate the optimal estimates of the unknown parameters. This concern naturally leads to the exploration of other search techniques. In particular, Genetic Algorithms (GAs), as population-based global optimization techniques that emulate natural genetic operators, have been introduced into the field of parameter estimation. In this paper, hybrid parameter estimation technique is developed to improve computational efficiency and accuracy of pure GA-based estimation. The proposed strategy integrates a GA and the Maximum Likelihood Estimation. Choices of input signals and estimation criterion are discussed involving an extensive sensitivity analysis. Experiment-related aspects, such as imperfection of data acquisition, are also considered. Computer simulation results reveal that the hybrid parameter estimation method proposed in this study shows great potential to outperform conventional techniques and pure GAs in accuracy, efficiency, as well as robustness with respect to the initial guesses and measurement uncertainty. Primary experimental validation is also implemented including interpretation and processing of field test data, as well as analysis of errors associated with aspects of experiment design. To provide more guidelines for implementing the hybrid GA approach, some practical guidelines on application of the proposed parameter estimation strategy are discussed.
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Garavello, A., M. Russo, Claudio Comis da Ronco, R. Ponza, and E. Benini. "Aerodynamic Shape Optimization of Air-Intakes of a Helicopter Turboshaft." In ASME 2012 Gas Turbine India Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gtindia2012-9506.

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The research project HEAVYcOPTer, a sub task of the European R&D program Clean-Sky GRC2 [1], is devoted to the efficient design and the shape optimization of the Agusta Westland AW101 helicopter turboshaft engine intake and exhaust system, to be carried out by means of advanced multi-objective optimization algorithms coupled with CFD Navier-Stokes solvers. The present paper describes the outcomes of HEAVYcOPTer in relation to the air intakes shape optimisation activities. This paper describes the technical details of such program. The optimisation method chosen for the redesign of the engine installation involves the application of the state of the art genetic algorithm GDEA, developed at the University of Padova and successfully applied in several fluid-dynamics applications, especially in the field of turbomachinery. For the present application, the set of geometrical designs constituting the genetic algorithm population are generated by means of morphing the original CFD model surface mesh: shapes are applied to baseline surface nodes with a displacement intensity driven by the GA chosen scaling factors. Then, CFD models of new designs are automatically generated and analyzed by the flow solver, returning to the GA the evaluation of the selected objective functions required in order to evolve the population in the next step of the evolutionary process. AW101 intakes have been optimised following a multi-objective/multi-point approach, minimizing inlet total pressure loss in both hovering and forward flight conditions simultaneously; optimised solutions were also constrained so as to not exceed the total pressure distortion level at the engine aerodynamic interface plane, so as to ensure inlet/engine compatibility with respect to the compressor surge limit. This approach ensured the improvement of the engine/airframe integration efficiency for the overall rotorcraft flight envelop, reducing fuel burn and increasing the helicopter propulsive efficiency.
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10

Hasanova, Aytakin. "PREDICTIVE GENETIC SCREENING." In The First International Scientific-Practical Conference- “Modern Tendencies of Dialogue in Multidenominational Society: philosophical, religious, legal view”. IRETC MTÜ, 2020. http://dx.doi.org/10.36962/mtdms202029.

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Human, as a species, is very variable, and his variability is at the basis of his social organization. This variability is maintained, in part, by the chance effects of gene assortment and the variation in these genes is the result of mutations in the past. If our remote ancestors had not mutated we would not he here; further, since no species is likely to he able to reduce its mutation rate substantially by the sort of selection to which it is exposed, we may regard mutations of recent origin as part of the price of having evolved. We are here: all of us have some imperfections we would wish not to have, and many of us are seriously incommoded by poor sight, hearing or thinking. Others among us suffer from some malformation due to faulty development. A few are formed lacking some essential substance necessary to metabolize a normal diet, to clot the blood, or to darken the back of the eye. We will all die and our deaths will normally be related to some variation in our immu-nological defences, in our ability to maintain our arteries free from occlusion, or in some other physiological aptitude. This massive variation, which is the consequence both of chance in the distribution of alleles and variety in the alleles themselves, imposes severe disabilities and handicaps on a substantial proportion of our population. The prospects of reducing this burden by artificial selection from counsel¬ling or selective feticide will be considered and some numerical estimates made of its efficiency and efficacy. Screening is a procedure by which populations are separated into groups, and is widely used for administrative and other purposes. At birth all babies are sexed and divided into two groups. Later the educable majority is selected from the ineducable minority; later still screening continues for both administrative and medical purposes. Any procedure by which populations are sifted into distinct groups is a form of screening, the word being derived from the coarse filter used to separate earth and stones. In medicine its essential features are that the population to be screen¬ed is not knowingly in need of medical attention and the action is taken on behalf of this population for its essential good. A simple example is provided by cervical smear examination, the necessary rationale for which must be the haimless and reliable detection of precancerous changes which can be prevented from becoming irreversible. Any rational decision on the development of such a service must be based on a balance of good and harm and any question of priorities in relation to other services must be based on costing. The balance of good and harm is a value judgement of some complexity. In the example of cervical smears anxiety and the consequences of the occasional removal of a healthy uterus must be weighed against the benefits of the complete removal of a cancerous one, and such matters cannot be costed in monetary terms. In fact, even such an apparently simple procedure as cervical screening is full of unknowns and many of these unknowns can only be resolved by extensive and properly designed studies. In genetic screening the matter is even more complicated, since the screening is often vicarious; that is, one person is screened in order to make a prediction on what may happen to someone else, usually their children, who may be un¬conceived or unborn. Further, the action of such screening may not be designed to ameliorate disease, but to eliminate a fetus which has a high chance of an affliction, or to prevent a marriage in which there is a mutual predisposition to producing abnormal children. These considerations impose very considerable dif¬ferences, since the relative values placed on marriage, on having children within marriage, and on inducing abortion, vary widely between individuals and between societies.
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