In this way, valueable genetic sequences are copied and combined with eachother to produce new, maybe better solutions. The chromosome part selection and recombination differs between crossover algorithms. The purpose of mutation is to reintroduce divergence into a converging population. This is a tool in avoiding local maxima, which is a common problem in stochastic algorithms. The mutation can be thought of as an error in the reproduction process.
Mutation is applied in combination with crossover.
This GA part defines how new chromosomes will be put into the existing population. In general this can be done in at least two ways. The former deletion technique is called 'Delete All' and the latter 'Steady Delete'. The evaluation algorithm used is crucial to how well the GA manages to find a good solution. There are, however problems that are more tricky to give a good fitness evaluation algorithm.