Genetic Algorithms - Mutation Mutation is one of the genetic operators used in a genetic algorithm to introduce diversity into the population and prevent premature convergence. It works by randomly altering the genes of one or more individuals in the population. This randomization can take the form of adding, deleting, or changing specific values or traits within the individual's gene structure. The mutation rate, or the probability of a mutation occurring, is a hyperparameter that is set by the user and can be adjusted depending on the requirements of the problem being solved. Mutation helps to prevent the algorithm from getting stuck in a local optimum by introducing new, potentially better solutions into the population. However, excessive mutation can lead to decreased performance as it may result in the creation of unfit individuals. Therefore, a careful balance must be struck between mutation and other genetic operators, such as selection and crossover, to achieve optimal results.