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The purpose of mutation in EAs 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 EAs 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.cite-ref-1[1]

The following requirements apply to all mutation operators used in an EA:cite-ref-2[2]cite-ref-3-3-0[3]

1. every point in the search space must be reachable by one or more mutations.

2. there must be no preference for parts or directions in the search space (no drift).

3. small mutations should be more probable than large ones.

For different genome types, different mutation types are suitable. Some mutations are Gaussian, Uniform, Zigzag, Scramble, Insertion, Inversion, Swap, and so on.cite-ref-4[4]cite-ref-5[5]cite-ref-6[6] An overview and more operators than those presented below can be found in the introductory book by Eiben and Smithcite-ref-7[7] or in.cite-ref-3-3-1[3]cite-ref-0-8-0[8]

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