A Comparative Study of a Penalty Function , a Repair

In this paper we compare the eeects of using various stochas-tic operators with the non-unicost set-covering problem. Four diierent crossover operators are compared to a repair heuristic which consists in transforming infeasible strings into feasible ones. These stochastic operators are incorporated in GENEsYs, the genetic algorithm we apply to problem instances of the set-covering problem we draw from well known test problems. GENEsYs uses a simple tness function that has a graded penalty term to penalize infeasibly bred strings. The results are compared to a non GA-based algorithm based on the greedy technique. Our computational results are then compared, shedding some light on the eeects of using diierent operators, a penalty function, and a repair heuristic on a highly constrained combinatorial optimization problem.