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.

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