As with most of the other aspects of a genetic algorithm, mutation has a loose analogy to a real biological process. In biology, mutation is a random change in an individual’s DNA sequence that often manifests itself in physical traits. For example, if you have blue eyes, you can thank genetic mutation.
Mutation in genetic algorithms works in much the same way. It’s a random change to some or all of the genes in a chromosome. The purpose of mutation is to introduce genetic diversity into the population.
If you recall from Chapter 1, Writing Your First Genetic Algorithm, the algorithm you wrote to solve the One-Max problem struggled to find the best solution until you added mutation. When dealing with binary genotypes, ...