GAs solve problems by maintaining and stochastically modifying a population of candidate solutions through the application of genetic operators in generating optimal solutions. During this process, a GA explores multiple potentially promising regions in the solution space at the same time, and switches stochastically from one region to another for performance improvement.
It is commonly believed that crossover is the major operator of GAs, with mutation for preventing the population from early convergence to a certain solution before an extensive exploration of other candidate solutions is made (Goldberg, 1989; Holland, 1992a). According to Holland (1992a), it is the crossover operator that enables GAs to focus on the most promising regions in a solution space, and mutation alone does not advance the search for a solution (Goldberg, 1989). Spears (1993) also maintains that crossover is a more robust constructor of new candidate solutions than mutation. In addition, in Herrera & Lozano’s (2000) gradual distributed GA model, mutation was embedded in the crossover operator.