On Designing Genetic Algorithms for Solving Small- and Medium-Scale Traveling Salesman Problems

Genetic operators are used in genetic algorithms (GA) to generate individuals for the new population. Much research focuses on finding most suitable operators for applications or on solving large-scale problems. However, rarely research addresses the performance of different operators in small- or medium-scale problems. This paper studies the impact of genetic operators on solving the traveling salesman problem (TSP). Using permutation coding, a number of different GAs are designed and analyzed with respect to the impact on the global search capability and convergence rate for small- and medium-scale TSPs. In addition, the differences between small- and medium-scale TSPs on suitable GA design are studied. The experiments indicate that the inversion mutation produces better solutions if combined with insertion mutation. Dividing the population into small groups does generate better results in medium-scale TSP; on the contrary, it is better to apply operators to the whole population in case of small-scale TSP.

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