Mutation (genetic algorithm)

Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next. It is analogous to biological mutation.

The classic example of a mutation operator 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 sequence. This random variable tells whether or not a particular bit will be flipped. This mutation procedure, based on the biological point mutation, 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 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.[1]

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:
1 0 1 0 0 1 0
1 0 1 0 1 1 0
The probability of a mutation of a bit is , where is the length of the binary vector. Thus, a mutation rate of 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. [2]

See alsoEdit

ReferencesEdit

  1. ^ "XI. Crossover and Mutation". http://www.obitko.com/: Marek Obitko. Retrieved 2011-04-07.
  2. ^ 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

BibliographyEdit