The best individual in the final population produced can be a highly evolved solution to the problem. The most important parts of a GA is crossover, mutation, population deletion, and chromosome fitness evaluation. This is the prime distinguishing factor of the Genetic Algorithm from other optimization algorithms. The role of crossover is to allow advantageous traits to be spread throughout the population in order that the population as whole may benefit from this chance discovery. This models sexual reproduction in nature.
Crossover is applied when a new offspring chromosome, based on two parent chromosomes, is created.
The algorithm selects one part from the first parent chromosome and another part from the other parent and the combines them, resulting in the new offspring chromosome. 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.