Genetic Algorithms Applications to Set Covering and Traveling Salesman Problems

For set covering problems, genetic algorithms with two types of crossover operators are investigated in conjunction with three penalty function and two multiobjective formulations. A Pareto multiobjective formulation and greedy crossover are suggested to work well. On the other hand, for traveling salesman problems, the results appear to be discouraging; genetic algorithm performance hardly exceeds that of a simple swapping rule. These results suggest that genetic algorithms have their place in optimization of constrained problems. However, lack of, or insufficient use of fundamental building blocks seems to keep the tested genetic algorithm variants from being competitive with specialized search algorithms on ordering problems.

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