As with the Simulated Annealing algorithm, the procedures controlling the generation of new solutions are so simple that the computational cost of implementing a GA is usually dominated by that associated with the evaluation of the problem functions. It is therefore important that these evaluations should be performed efficiently and essential if the optimization is to be performed on a serial computer. Advice on how these calculations can be accelerated can be found in the chapter appropriate to the form of the system equations to be solved.

Unlike SA, which is intrinsically a sequential algorithm, GAs are particularly well-suited to implementation on parallel computers. Evaluation of the objective function and constraints can be done simultaneously for a whole population, as can the production of the new population by mutation and crossover. Thus, on a highly parallel machine, a GA can be expected to run nearly N times as fast for many problems, where N is the population size.