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It is analogous to biological mutation. The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a probability that an arbitrary bit in a genetic sequence will be flipped from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a ...
Nevertheless, the concept is still widely used in evolutionary genetics, e.g. to explain the persistence of deleterious alleles as in the case of spinal muscular atrophy, [5] [4] or, in theoretical models, mutation-selection balance can appear in a variety of ways and has even been applied to beneficial mutations (i.e. balance between selective ...
Loss-of-function mutations, also called inactivating mutations, result in the gene product having less or no function (being partially or wholly inactivated). When the allele has a complete loss of function ( null allele ), it is often called an amorph or amorphic mutation in Muller's morphs schema.
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 evolutionary ...
Random mutations are the ultimate source of genetic variation. Mutations are likely to be rare, and most mutations are neutral or deleterious, but in some instances, the new alleles can be favored by natural selection. Polyploidy is an example of chromosomal mutation. Polyploidy is a condition wherein organisms have three or more sets of ...
Types of mutations that can be introduced by random, site-directed, combinatorial, or insertional mutagenesis. In molecular biology, mutagenesis is an important laboratory technique whereby DNA mutations are deliberately engineered to produce libraries of mutant genes, proteins, strains of bacteria, or other genetically modified organisms. The ...
The latter retains some random mutations in the gene pool due to the systematically improved chance for survival and reproduction that those mutated genes confer on individuals who possess them. The location of the mutation is not entirely random however as e.g. biologically important regions may be more protected from mutations. [14] [15] [16]
While each of these steps are individually reversible (for example, A may regain the capacity to function independently or the A:B interaction may be lost), a random sequence of mutations tends to further reduce the capacity of A to function independently and a random walk through the dependency space may very well result in a configuration in ...