AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Solving TSP with Shuffled Frog-Leaping Algorithm
ISDA '08 Proceedings of the 2008 Eighth International Conference on Intelligent Systems Design and Applications - Volume 03
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An evolutionary algorithm for dynamic multi-objective TSP
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IEEE Transactions on Evolutionary Computation
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
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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.