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Setting aside other factors (e.g., balancing selection, and genetic drift), the equilibrium number of deleterious alleles is then determined by a balance between the deleterious mutation rate and the rate at which selection purges those mutations. Mutation–selection balance was originally proposed to explain how genetic variation is ...
The Haldane-Muller theorem of mutation–selection balance says that the load depends only on the deleterious mutation rate and not on the selection coefficient. [6] Specifically, relative to an ideal genotype of fitness 1, the mean population fitness is exp ( − U ) {\displaystyle \exp(-U)} where U is the total deleterious mutation rate ...
Mutation is a genetic operator used to maintain genetic diversity of the chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological mutation .
Selection acts on variation in phenotypes, which are often the result of mutations in protein-coding genes. The genetic code is written in DNA sequences as codons , groups of three nucleotides . Each codon represents a single amino acid in a protein chain.
Selection coefficient, usually denoted by the letter s, is a measure used in population genetics to quantify the relative fitness of a genotype compared to other genotypes. . Selection coefficients are central to the quantitative description of evolution, since fitness differences determine the change in genotype frequencies attributable to selecti
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 ...
The adaptive value can be measured by contribution of an individual to the gene pool of their offspring. The adaptive values are approximately calculated from the rates of change in frequency and mutation–selection balance. [2]
If the selection and crossover operators are used without the mutation operator, the algorithm will tend to converge to a local minimum, that is, a good but sub-optimal solution to the problem. Using the mutation operator on its own leads to a random walk through the search space. Only by using all three operators together can the genetic ...