These building blocks represent the most fit genetic structures in the population. Mutation is used because the recombination process alone cannot explore search space sections not represented in the population's genetic structures. This could make the search become stuck around local minima. The mutation operator introduces new genetic structures in the population by randomly changing some of its building blocks, helping the algorithm escape local minima traps. Since the modification is totally random and thus not related to any previous genetic structures present in the population, it creates different structures related to other sections of the search space.
Mutation is implemented by occasionally altering a random part of a chromosome.
A number of other operators, other than crossover and mutation, have been introduced since the basic model was proposed. They are usually versions of the recombination and genetic alteration processes adapted to constraints of a particular problem. This description of the genetic algorithm's computational model reviews the steps needed to create the algorithm. It is desirable that the encoding makes the representation as robust as possible. This means that even if a piece of the representation is randomly changed, in the majority of cases, it should still represent a viable individual.