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The rate of evolution is quantified as the speed of genetic or morphological change in a lineage over a period of time. The speed at which a molecular entity (such as a protein, gene, etc.) evolves is of considerable interest in evolutionary biology since determining the evolutionary rate is the first step in characterizing its evolution. [1]
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.
In computer science, simulations of evolution using evolutionary algorithms and artificial life started in the 1960s and were extended with simulation of artificial selection. [253] Artificial evolution became a widely recognised optimisation method as a result of the work of Ingo Rechenberg in the 1960s.
The basis for selection is the quality of an individual, which is determined by the fitness function. In memetic algorithms, an extension of EA, selection also takes place in the selection of those offspring that are to be improved with the help of a meme (e.g. a heuristic).
The existence of limits in artificial selection experiments was discussed in the scientific literature in the 1940s or earlier. [1] The most obvious possible cause of reaching a limit (or plateau) when a population is under continued directional selection is that all of the additive-genetic variation (see additive genetic effects) related to that trait gets "used up" or fixed. [2]
These charts depict the different types of genetic selection. On each graph, the x-axis variable is the type of phenotypic trait and the y-axis variable is the amount of organisms. Group A is the original population and Group B is the population after selection. Graph 1 shows directional selection, in which a single extreme phenotype is favored.
Evolutionary programming – Similar to evolution strategy, but with a deterministic selection of all parents. Evolution strategy (ES) – Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates. The method is mainly used for numerical optimization, although there are also variants for ...
If the primary effect of natural selection on the evolution of the sequences is to constrain some sites, then models of among-site rate-heterogeneity can be used. This approach allows one to estimate only one matrix of relative rates of substitution, and another set of parameters describing the variance in the total rate of substitution across ...