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Estimation of quantitative genetic parameters [PDF]
Philosophical Transactions of the Royal Society B: Biological Sciences, 2005 This paper gives a short review of the development of genetic parameter estimation over the last 40 years. This shows the development of more statistically and computationally efficient methods that allow the fitting of more biologically appropriate models. Methods have evolved from direct methods based on covariances between relatives to methods based
Thompson, R. +2 more
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Estimation of quantitative genetic parameters [PDF]
Proceedings of the Royal Society B: Biological Sciences, 2008 This paper gives a short review of the development of genetic parameter estimation over the last 40 years. The need to analyse genetic processes in both animal selection experiments and animal breeding improvement programmes motivated the majority of this work.
Thompson, R.
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COMPARISON OF QUANTITATIVE GENETIC PARAMETERS [PDF]
Evolution, 1992 Shaw (1991) has described a maximum-likelihood procedure for comparing genetic covariance matrices between populations. While her efforts to expand the procedures available for quantitative genetic analyses are laudable, her discussion is incomplete in several respects. For example, consider her criticism of Dietz's matrix permutation test (Dietz, 1983)
David E, Cowley, William R, Atchley
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Genetic parameters and genetic trends in the Chinese × European Tiameslan composite pig line. I. Genetic parameters [PDF]
Genetics Selection Evolution, 2000 Genetic parameters of body weight at 4 (W4w), 8 (W8w) and 22 (W22w) weeks of age, days from 20 to 100 kg (DT), average backfat thickness at 100 kg (ABT), teat number (TEAT), number of good teats (GTEAT), total number of piglets born (TNB), born alive (NBA) and weaned (NW) per litter, and birth to weaning survival rate (SURV) were estimated in the ...
Zhang, Siqing +4 more
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Dynamic Parameter Encoding for Genetic Algorithms [PDF]
Machine Learning, 1992 The common use of static binary place-value codes for real-valued parameters of the phenotype in Holland's genetic algorithm (GA) forces either the sacrifice of representational precision for efficiency of search or vice versa. Dynamic Parameter Encoding (DPE) is a mechanism that avoids this dilemma by using convergence statistics derived from the GA ...
Nicol N. Schraudolph, Richard K. Belew
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Cosmological Parameter Estimation with Genetic Algorithms
Universe, 2023 Genetic algorithms are a powerful tool in optimization for single and multimodal functions. This paper provides an overview of their fundamentals with some analytical examples. In addition, we explore how they can be used as a parameter estimation tool in cosmological models to maximize the likelihood function, complementing the analysis with the ...
Ricardo Medel-Esquivel +4 more
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Operator and parameter adaptation in genetic algorithms [PDF]
Soft Computing, 1997 Genetic Algorithms are a class of powerful, robust search techniques based on genetic inheritance and the Darwinian metaphor of “Natural Selection”. These algorithms maintain a finite memory of individual points on the search landscape known as the “population”.
Jim Smith 0002, Terence C. Fogarty
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Loading as a design parameter for genetic circuits [PDF]
2016 American Control Conference (ACC), 2016 A significant problem when building complex biomolecular circuits is due to context-dependence: the dynamics of a system are altered upon changes to its context, potentially degrading the system's performance. Here, we study retroactivity, a specific type of context-dependence, by analyzing the effects of loads on a transcription factor applied by the ...
Nithin Senthur Kumar +1 more
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Journal of Dairy Science, 1968 Abstract Estimates were made of some genetic parameters of milk yield and its components and of the breeding values of sires for yields and constituent percentages. The data consisted of 1.449 cows of the Ayrshire, Guernsey, Holstein, Jersey, and Brown Swiss breeds located in 43 herds.
M C, Gacula, S N, Gaunt, R A, Damon
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The Complex Parameter Landscape of the Compact Genetic Algorithm [PDF]
Algorithmica, 2020 AbstractThe compact Genetic Algorithm (cGA) evolves a probability distribution favoring optimal solutions in the underlying search space by repeatedly sampling from the distribution and updating it according to promising samples. We study the intricate dynamics of the cGA on the test functionOneMax, and how its performance depends on the hypothetical ...
Johannes Lengler +2 more
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