Mutation
In genetic algorithms and evolutionary computation, mutation is a genetic operator used to maintain a diversity from one generation of a population to the next. It is analogous to biological mutation. Mutation alters one or more gene values in a chromosome from its initial state. The purpose of mutation is to introduce diversity into the sampled population.
List of the evolutionary strategy strategy mutation operations:
Missing docstring for isotropicSigma. Check Documenter's build log for details.
Missing docstring for anisotropicSigma. Check Documenter's build log for details.
List of the evolutionary strategy population mutation operations:
Missing docstring for isotropic. Check Documenter's build log for details.
Missing docstring for anisotropic. Check Documenter's build log for details.
List of the binary mutation operations:
Evolutionary.flip — Functionflip(recombinant)Returns a binary recombinant with a bit flips at random positions.
Evolutionary.bitinversion — Functionbitinversion(recombinant)Returns a binary recombinant with its bits inverted.
List of the real valued mutation operations:
Evolutionary.domainrange — Functiondomainrange(valrange, m = 20)Returns a real valued mutation function with the mutation range valrange and the mutation probability 1/m [1].
List of the combinatorial mutation operations (applicable to binary vectors):
Missing docstring for inversion. Check Documenter's build log for details.
Missing docstring for insertion. Check Documenter's build log for details.
Missing docstring for swap2. Check Documenter's build log for details.
Missing docstring for scramble. Check Documenter's build log for details.
Missing docstring for shifting. Check Documenter's build log for details.
References
- 1Mühlenbein, H. and Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization. Evolutionary Computation, 1 (1), pp. 25-49, 1993.