Mutation is a

genetic operator A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one anothe ...

used to maintain genetic diversity from one generation of a population of

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 ...

chromosomes A chromosome is a long DNA molecule with part or all of the genetic material of an organism. In most chromosomes the very long thin DNA fibers are coated with packaging proteins; in eukaryotic cells the most important of these proteins are ...

to the next. It is analogous to biological

mutation In biology, a mutation is an alteration in the nucleic acid sequence of the genome of an organism, virus, or extrachromosomal DNA. Viral genomes contain either DNA or RNA. Mutations result from errors during DNA or viral replication, mi ...

. The classic example of a mutation operator involves a probability that an arbitrary

bit The bit is the most basic unit of information in computing and digital communications. The name is a portmanteau of binary digit. The bit represents a logical state with one of two possible values. These values are most commonly represente ...

in a

genetic sequence A nucleic acid sequence is a succession of bases signified by a series of a set of five different letters that indicate the order of nucleotides forming alleles within a DNA (using GACT) or RNA (GACU) molecule. By convention, sequences are us ...

will be flipped from its original state. A common method of implementing the mutation operator involves generating a

random variable A random variable (also called random quantity, aleatory variable, or stochastic variable) is a mathematical formalization of a quantity or object which depends on random events. It is a mapping or a function from possible outcomes (e.g., the po ...

for each bit in a sequence. This random variable tells whether or not a particular bit will be flipped. This mutation procedure, based on the biological

point mutation A point mutation is a genetic mutation where a single nucleotide base is changed, inserted or deleted from a DNA or RNA sequence of an organism's genome. Point mutations have a variety of effects on the downstream protein product—consequence ...

, is called single point mutation. Other types are inversion and floating point mutation. When the gene encoding is restrictive as in permutation problems, mutations are swaps, inversions, and scrambles. The purpose of mutation in GAs is to introduce diversity into the sampled population. Mutation operators are used in an attempt to avoid

local minima In mathematical analysis, the maxima and minima (the respective plurals of maximum and minimum) of a function, known collectively as extrema (the plural of extremum), are the largest and smallest value of the function, either within a given ran ...

by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping convergence to the global optimum. This reasoning also leads most GA systems to avoid only taking the fittest of the population in generating the next generation, but rather selecting a random (or semi-random) set with a weighting toward those that are fitter. For different genome types, different mutation types are suitable: * Bit string mutation ::The mutation of bit strings ensue through bit flips at random positions. ::Example: :: ::The probability of a mutation of a bit is

\frac

, where

l

is the length of the binary vector. Thus, a mutation rate of

1

per mutation and individual selected for mutation is reached. * Shrink ::This operator adds a random number taken from a Gaussian distribution with mean equal to the original value of each decision variable characterizing the entry parent vector. Claudio Comis Da Ronco, Ernesto Benini, A Simplex-Crossover-Based Multi-Objective Evolutionary Algorithm, IAENG Transactions on Engineering Technologies, Volume 247 of the series Lecture Notes in Electrical Engineering pp 583-598, 2013 https://link.springer.com/chapter/10.1007%2F978-94-007-6818-5_41

References

Bibliography

* John Holland, Adaptation in Natural and Artificial Systems,

University of Michigan Press The University of Michigan Press is part of Michigan Publishing at the University of Michigan Library. It publishes 170 new titles each year in the humanities and social sciences. Titles from the press have earned numerous awards, including ...

, Ann Arbor, Michigan. 1975. . {{DEFAULTSORT:Mutation (Genetic Algorithm) Genetic algorithms

See also

References

Bibliography