The best parameters depends of the constraints of the problem (time window, capacity, ....)

But, for me, the mutation probability never exceeds 0.01 for this problem. You can use an adaptative probability to force the exploration at the begening and reduce this probability with the convergence.

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an easy starting point (with complete java code !) :

http://www.theprojectspot.com/tutorial-post/applying-a-genetic-algorithm-to-the-travelling-salesman-problem/5

a more comprehensive review :

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

P. Larrañaga , C. M. H. Kuijpers , R.H. Murga , I. Inza , S. Dizdarevic

Artificial Intelligence Review, 1999

http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.8882

.Abstract - This paper is the result of a literature study carried out by the authors. It is a review of the different attempts made to solve the Travelling Salesman Problem with Genetic Algorithms. We present crossover and mutation operators, developed to tackle the Travelling Salesman Problem with Genetic Algorithms with different representations such as: binary representation, path representation, adjacency representation, ordinal representation and matrix representation. Likewise, we show the experimental results obtained with different standard examples using combination of crossover and mutation operators in relation with path representation.

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A recent paper on the application of Evolutionary Algorithms for the TSP. Maybe it might be interesting for you (the code is available on demand):

@inproceedings{Segura:2015:NDE:2739480.2754802,

author = {Segura, Carlos and Botello Rionda, Salvador and Hern\'{a}ndez Aguirre, Arturo and Valdez Pe\~{n}a, S. Ivvan},

title = {A Novel Diversity-based Evolutionary Algorithm for the Traveling Salesman Problem},

booktitle = {Proceedings of the 2015 on Genetic and Evolutionary Computation Conference},

series = {GECCO '15},

year = {2015},

isbn = {978-1-4503-3472-3},

location = {Madrid, Spain},

pages = {489--496},

numpages = {8},

url = {http://doi.acm.org/10.1145/2739480.2754802},

doi = {10.1145/2739480.2754802},

acmid = {2754802},

publisher = {ACM},

address = {New York, NY, USA},

keywords = {diversity preservation, evolutionary algorithms, memetic algorithms, traveling salesman problem},

}

Hi, you can use of this code:

Traveling Salesman Problem - Genetic Algorithm