tsp-genetic-algorithms

Solving Traveling Salesman Problem (TSP) via Genetic Algorithms (GAs).

Tools

Languages/frameworks

• Python 3
• Fortran 90
• f2py
• NumPy

Tools

• Argparse
• Matplotlib
• Jupyter notebook

Some results

Comparison of performance for several crossover operators

Mutation probability behavior with adaptive control

References

1. Deep, Kusum, and Hadush Mebrahtu. "New variations of order crossover for travelling salesman problem." International Journal of Combinatorial Optimization Problems and Informatics 2.1 (2011): 2-13.
2. Potvin, Jean-Yves. "Genetic algorithms for the traveling salesman problem." Annals of Operations Research 63 (1996): 337-370.
3. Hussain, Abid, et al. "Genetic algorithm for traveling salesman problem with modified cycle crossover operator." Computational intelligence and neuroscience 2017 (2017).
4. Kumar, Rakesh, Girdhar Gopal, and Rajesh Kumar. "Novel crossover operator for genetic algorithm for permutation problems."International Journal of Soft Computing and Engineering (IJSCE) 3.2 (2013): 252-258.
5. Abdoun, Otman, Jaafar Abouchabaka, and Chakir Tajani. "Analyzing the performance of mutation operators to solve the travelling salesman problem." arXiv preprint arXiv:1203.3099 (2012).
6. Hassanat, Ahmad, et al. "Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach." Information 10.12 (2019): 390.
7. f2py official documentation: https://numpy.org/doc/stable/f2py/
8. Goldberg, David E., and Robert Lingle. "Alleles, loci, and the traveling salesman problem." Proceedings of the first international conference on genetic algorithms and their applications. Psychology Press, 2014.
9. Davis, Lawrence. "Applying adaptive algorithms to epistatic domains." IJCAI. Vol. 85. 1985.