A New Selection Operator - CSM in Genetic Algorithms for Solving the TSP

Genetic Algorithms (GAs) is a type of local search that mimics biological evolution by taking a population of string, which encodes possible solutions and combines them based on fitness values to produce individuals that are fitter than others. One of the most important operators in Genetic Algorithm is the selection operator. A new selection operator has been proposed in this paper, which is called Clustering Selection Method (CSM). The proposed method was implemented and tested on the traveling salesman problem. The proposed CSM was tested and compared with other selection methods, such as random selection, roulette wheel selection and tournament selection methods. The results showed that the CSM has the best results since it reached the optimal path with only 8840 iterations and with minimum distance which was 79.7234 when the system has been applied for solving Traveling Salesman Problem (TSP) of 100 cities.

@article{Alkhayri2016,
  title     = {A New Selection Operator - CSM in Genetic Algorithms for Solving the TSP},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {10},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Wael Raef Alkhayri and Suhail Sami Owais and Mohammad Shkoukani},
  doi       = {10.14569/IJACSA.2016.071008},
  url       = {https://doi.org/10.14569/IJACSA.2016.071008}
}