Genetic operator

There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful.

Genetic operators used in evolutionary algorithms are analogous to those in the natural world: survival of the fittest, or selection; reproduction (crossover, also called recombination); and mutation.

Selection operators give preference to better candidate solutions (chromosomes), allowing them to pass on their 'genes' to the next generation (iteration) of the algorithm.

As with the crossover operator, the mutation method is usually chosen to match the representation of the solution within the chromosome.

[15][3] While each operator acts to improve the solutions produced by the evolutionary algorithm working individually, the operators must work in conjunction with each other for the algorithm to be successful in finding a good solution.

Using the mutation operator on its own leads to a random walk through the search space.