simulated annealing can be seen as a genetic algorithm, where there is no crossover, the mutation rate changes with time, and the population (size of the set of the search candidates) is only one.

Going the other way, if we make the population larger and permit crossover, we get genetic simulated annealing (GSA), which can be an effective method of nonlinear optimization, combining the best parts of genetic algorithms and simulated annealing.