Truncation Selection

	Truncation Selection In animal and plant breeding, truncation selection is a standard method in selective breeding in selecting animals to be bred for the next generation. Animals are ranked by their phenotypic value on some trait such as milk production, and the top percentage is reproduced. The effects of truncation selection for a continuous trait can be modeled by the standard breeder's equation by using heritability and truncated normal distributions. On a binary trait, it can be modeled easily using the liability threshold model. It is considered an easy and efficient method of breeding. Computer science In computer science, truncation selection is a selection method used in genetic algorithm In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to gene ...s to select potential candidate solutions for recombin ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Selective Breeding Selective breeding (also called artificial selection) is the process by which humans use animal breeding and plant breeding to selectively develop particular phenotypic traits (characteristics) by choosing which typically animal or plant males and females will sexually reproduce and have offspring together. Domesticated animals are known as breeds, normally bred by a professional breeder, while domesticated plants are known as varieties, cultigens, cultivars, or breeds. Two purebred animals of different breeds produce a crossbreed, and crossbred plants are called hybrids. Flowers, vegetables and fruit-trees may be bred by amateurs and commercial or non-commercial professionals: major crops are usually the provenance of the professionals. In animal breeding, techniques such as inbreeding, linebreeding, and outcrossing are utilized. In plant breeding, similar methods are used. Charles Darwin discussed how selective breeding had been successful in producing change over time in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Breeder's Equation Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of ''variation'' in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is ''not'' explained by the environment or random chance?" Other causes of measured variation in a trait are characterized as environmental factors, including observational error. In human studies of heritability these are often apportioned into factors from "shared environment" and "non-shared environment" based on whether they tend to result in persons brought up in the same household being more or less similar to persons who were not. Heritability is estimated by comparing individual phenotypic variation among related individuals in a population, by examining the association between indivi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Heritability Heritability is a statistic used in the fields of breeding and genetics that estimates the degree of ''variation'' in a phenotypic trait in a population that is due to genetic variation between individuals in that population. The concept of heritability can be expressed in the form of the following question: "What is the proportion of the variation in a given trait within a population that is ''not'' explained by the environment or random chance?" Other causes of measured variation in a trait are characterized as environmental factors, including observational error. In human studies of heritability these are often apportioned into factors from "shared environment" and "non-shared environment" based on whether they tend to result in persons brought up in the same household being more or less similar to persons who were not. Heritability is estimated by comparing individual phenotypic variation among related individuals in a population, by examining the association between ind ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Truncated Normal Distribution In probability and statistics, the truncated normal distribution is the probability distribution derived from that of a normally distributed random variable by bounding the random variable from either below or above (or both). The truncated normal distribution has wide applications in statistics and econometrics. Definitions Suppose X has a normal distribution with mean \mu and variance \sigma^2 and lies within the interval (a,b), \text \; -\infty \leq a < b \leq \infty . Then $X$ conditional on $a < X < b$ has a truncated normal distribution. Its probability density function , $f$ , for $a \leq x \leq b$ , is given by : $f(x;\mu,\sigma,a,b) = \frac\,\frac$ and by $f=0$ otherwise. Here, : $\phi(\xi)=\frac\exp\left(-\frac\xi^2\right)$ is t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Liability Threshold Model In mathematical or statistical modeling a threshold model is any model where a threshold value, or set of threshold values, is used to distinguish ranges of values where the behaviour predicted by the model varies in some important way. A particularly important instance arises in toxicology, where the model for the effect of a drug may be that there is zero effect for a dose below a critical or threshold value, while an effect of some significance exists above that value.Dodge, Y. (2003) ''The Oxford Dictionary of Statistical Terms'', OUP. Certain types of regression model may include threshold effects. Collective behavior Threshold models are often used to model the behavior of groups, ranging from social insects to animal herds to human society. Classic threshold models were introduced by Sakoda, in his 1949 dissertation and the Journal of Mathematical Sociology (JMS vol 1 #1, 1971). They were subsequently developed by Schelling, Axelrod, and Granovetter to model collective be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Selection (genetic Algorithm) Selection is the stage of a genetic algorithm in which individual genomes are chosen from a population for later breeding (using the crossover operator). A generic selection procedure may be implemented as follows: #The fitness function is evaluated for each individual, providing fitness values, which are then normalized. Normalization means dividing the fitness value of each individual by the sum of all fitness values, so that the sum of all resulting fitness values equals 1. #Accumulated normalized fitness values are computed: the accumulated fitness value of an individual is the sum of its own fitness value plus the fitness values of all the previous individuals; the accumulated fitness of the last individual should be 1, otherwise something went wrong in the normalization step. #A random number ''R'' between 0 and 1 is chosen. #The selected individual is the first one whose accumulated normalized value is greater than or equal to ''R''. For many problems the above algorithm m ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Genetic Algorithm In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Breeder Genetic Algorithm A breeder is a person who selectively breeds carefully selected mates, normally of the same breed to sexually reproduce offspring with specific, consistently replicable qualities and characteristics. This might be as a farmer, agriculturalist, or hobbyist, and can be practiced on a large or small scale, for food, fun, or profit. About A breeder can breed purebred pets such as cats or dogs, livestock such as cattle or horses, and may show their animals professionally in assorted forms of competitions In these specific instances, the breeder strives to meet standards in each animal set out by organizations. A breeder may also assist with breeding animals in the zoo. In other cases, a breeder can be referred to an animal scientist who has the capabilities of developing more efficient ways to produce the meat and other animal products humans eat. Earnings as a breeder vary widely because of the various types of work involved in the job title. Even in breeding small domestic animal ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Genetic Algorithms In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, etc. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]