With a genetically converged population, sections of genotype that are swapped in recombination are likely to be fairly similar. With species evolution, recombination does not have the prime significance it has in standard GAs -- asexual evolution is indeed feasible -- but nevertheless it is a useful genetic operator.
There are two rôles recombination has which are opposite sides of the same coin. On the one hand, it allows two fortunate mutations which happen to have occurred independently in two different lineages within the population to be combined into one which has both: something not possible with asexual reproduction. On the other hand, it allows parents with a detrimental mutation to produce an offspring which does not have it: also impossible asexually, in the absence of highly improbable back-mutations. This latter effect in general allows higher mutation rates to be used with recombination than were suggested above for asexual populations, thus promoting exploration without risking loss of a currently achieved local optimum.
Recombination is particularly powerful when combined with a distributed GA. Here each member of the population is allocated a different position in some notional geographical space, often a two-dimensional toroidal grid. Recombination between individuals is only allowed for pairs within a certain distance of each other on this grid, which thus comprises a number of overlapping neighbourhoods. This combines the virtues of small and large populations; small interrelated local populations allow through random drift more extended search through genotype space, but the overlapping nature of such localities means that any improvement found percolates through the whole population.
Recombination can run into problems with genotypes of differing lengths; it may not be clear, given a crossover or recombination point in one parent, where a corresponding crossover should be made in the other parent. Whatever system is used should ensure that homologous segments of the genotype are swapped. Often this may need domain-specific GA program code; a general algorithm for long binary strings is given in [9].