Reckoning of a fitness value for each subject. It will directly depend on the distance to the optimum. Selection of the subjects that will mate according to their share in the population global fitness. Genomes crossover and mutations. And then start again from point 3.
Crossover is the basis of genetic algorithms, there is nevertheless other operators like mutation.
In fact, the desired solution may happen not to be present inside a given genetic pool, even a large one. Mutations allow the emergence of new genetic configurations which, by widening the pool improve the chances to find the optimal solution. Other operators like inversion are also possible, but we won't deal with them here. We simulate that way the adaptive pressure of the environment. The diversity of the genetic pool is then too reduced to allow the genetic algorithm to progress.