Genetic Algorithms Mutation - Genetic Algorithms

What is Genetic Algorithms Mutation?

Introduction to Mutation

In general terms, mutation can also be defined as a small random tweak in the DNA, to get a new solution. It is used to keep and present diversity in the genetic population and is typically applied with a low probability – pm. If the probability is very high, the GA gets reduced to a random search.

Mutation is the part of the GA which is related to the “exploration” of the search space. It has been observed that mutation is essential to the convergence of the GA while crossover is not.

Mutation Operators

In this segment, we define some of the most usually used mutation operators. Like the crossover operators, this is not an exhaustive list and the GA designer might find a combination of these approaches or a problem-specific mutation operator more useful.

Bit Flip Mutation

In this bit flip mutation, we select one or more random bits and flip them. This is used for binary encoded GAs.
bit_flip_mutation

Random Resetting

Random Resetting is an addition of the bit flip for the integer representation. In this, a random value from the set of permissible values is assigned to a randomly chosen gene.

Swap Mutation

In swap mutation, we select two positions on the chromosome at random, and interchange the values. This is common in permutation based encodings.

swap_mutation

Scramble Mutation

Scramble mutation is also widespread with permutation representations. In this, from the entire DNA, a subset of genes is chosen and their values are scrambled or shuffled randomly.

scramble_mutation

Inversion Mutation

In inversion mutation, we chose a subset of genes like in scramble mutation, but instead of shuffling the subset, we merely invert the entire string in the subset.

inversion_mutation

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