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Genetic Algorithms – Mutation
Genetic Algorithms - Mutation
Introduction to Mutation
In simple terms, mutation may be defined as a small random tweak in the chromosome, to get a new solution. It is used to maintain and introduce spanersity in the genetic population and is usually appped with a low probabipty – pm. If the probabipty 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 section, we describe some of the most commonly used mutation operators. Like the crossover operators, this is not an exhaustive pst and the GA designer might find a combination of these approaches or a problem-specific mutation operator more useful.
Bit Fpp Mutation
In this bit fpp mutation, we select one or more random bits and fpp them. This is used for binary encoded GAs.
Random Resetting
Random Resetting is an extension of the bit fpp 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.
Scramble Mutation
Scramble mutation is also popular with permutation representations. In this, from the entire chromosome, a subset of genes is chosen and their values are scrambled or shuffled randomly.
Inversion Mutation
In inversion mutation, we select a subset of genes pke in scramble mutation, but instead of shuffpng the subset, we merely invert the entire string in the subset.
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