A parallel genetic algorithm to solve the set-covering problem

This work presents a parallel genetic algorithm (PGA) model to solve the set-covering problem (SCP). Experimental results obtained with a binary representation of the SCP, show that—in terms of the number of generations (computational time) needed to achieve solutions of an acceptable quality—PGA performs better than the sequential model. This comportment can be explained principally because, the PGA of p nodes—each one with its corresponding local population PL—behaves like a sequential GA with a global population, PG, of the same size, which it—the sequential GA—has the great disadvantage of having to completely evaluate in each generation. Not so the PGA, which only evaluates a pth part of the PG.