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Fitness (often denoted w in population genetics models) is a central idea in evolutionary theory. It can be defined either with respect to a genotype or to a phenotype in a given environment. In either case, it describes the ability to both survive and reproduce, and is equal to the average contribution to the gene pool of the next generation that is made by an average individual of the specified genotype or phenotype. The term "Darwinian fitness" is often used to make clear the distinction with physical fitness. If differences between alleles of a given gene affect Darwinian fitness, then the frequencies of the alleles will change across generations; the alleles with higher fitness become more common. This process is called natural selection.An individual's fitness is manifested through its phenotype. The phenotype is affected by the developmental environment as well as by genes, and the fitness of a given phenotype can be different in different environments. The fitnesses of different individuals with the same genotype are therefore not necessarily equal. However, since the fitness of the genotype is an averaged quantity, it will reflect the reproductive outcomes of all individuals with that genotype in a given environment or set of environments.Inclusive fitness differs from individual fitness by including the ability of an allele in one individual to promote the survival and/or reproduction of other individuals that share that allele, in preference to individuals with a different allele. One mechanism of inclusive fitness is kin selection.
There are two commonly used measures of fitness; absolute fitness and relative fitness.
Absolute fitness (w_{\mathrm{abs}}) of a genotype is defined as the ratio between the number of individuals with that genotype after selection to those before selection. It is calculated for a single generation and must be calculated from absolute numbers. When the absolute fitness is larger than 1, the number of individuals bearing that genotype increases; an absolute fitness smaller than 1 indicates an absolute fall in the number of individuals bearing the genotype. If the number of individuals in a population stays constant, then the average absolute fitness must be equal to 1.
{w_{\mathrm{abs}}} = {{N_{\mathrm{after}}} \over {N_{\mathrm{before}}}}
Absolute fitness for a genotype can also be calculated as the product of the probability of survival multiplied by the average fecundity. Absolute fitness is used in Fisher's fundamental theorem.
Relative fitness is quantified as the average number of surviving progeny of a particular genotype compared with average number of surviving progeny of competing genotypes after a single generation, i.e. one genotype is normalized at w=1
In function optimization, fitness approximation is a method for decreasing the number of fitness function evaluations to reach a target solution. It belongs to the general class of evolutionary computation or artificial evolution methodologies.
In many real-world optimization problems including engineering problems, the number of fitness function evaluations needed to obtain a good solution dominates the optimization cost. In order to obtain efficient optimization algorithms, it is crucial to use prior information gained during the optimization process. Conceptually, a natural approach to utilizing the known prior information is building a model of the fitness function to assist in the selection of candidate solutions for evaluation. A variety of techniques for constructing of such a model, often also referred to as surrogates, metamodels or approximation models – for computationally expensive optimization problems have been considered.
Common approaches to constructing approximate models based on learning and interpolation from known fitness values of a small population include:
low-degree
Polynomials and regression models
Artificial neural networks including
Multilayer perceptrons
Radial basis function networks
Support vector machines
Due to the limited number of training samples and high dimensionality encountered in engineering design optimization, constructing a globally valid approximate model remains difficult. As a result, evolutionary algorithms using such approximate fitness functions may converge to local optima. Therefore, it can be beneficial to selectively use the original fitness function together with the approximate model.
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Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
Fitness.com Fitness Exercise for Women for Men for Women at Home for Men at Home Abs For Kids for Women to Lose Weight Tumblr Photos
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