genetic algorithm usually use a "mutation rate" to control the rate of chromosome mutation. Most of the researchers at researchgate recommend to keep this rate low in order to converge quickly, to be able to find local optima and not to make the optimization a random walk. However, I see one major problem with keeping a low mutation rate. If the breeding at one point doesn't result into "new" children/chromosomes, the algorithm will run very inefficiently. Assume that all individuals in the population have the same chromosome, then crossovers will result into the same individuals until a mutation occurs in one of the chromosomes. With a low rate this can take quite long (until then the fitness function with the same chromosomes is repated again and again).
Isn't it much more efficient to mutate on purpose as soon as children/chromosomes would repeat in the next generation in order to assure that we explore new solution space? Wouldn't this make a "mutation rate" obsolete and the entire algorithm more efficient or do I miss a major point here?