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Volume 8, Issue 1
Shared Crossover Method for Solving Traveling Salesman Problem

Mouhammd Al kasassbeh, A. Alabadleh and T. Al-Ramadeen

J. Info. Comput. Sci. , 8 (2013), pp. 055-062.

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  • Abstract
Genetic algorithms (GA) are evolutionary techniques that used crossover and mutation operators to solve optimization problems using a survival of the fittest idea. They have been used successfully in a variety of different problems, including the traveling salesman problem. The main idea of Traveling Salesman Problem (TSP) is to find the minimum traveling cost for visiting cities; the salesman must visit each city exactly once and return to the starting point of origin. Genetic algorithms are search methods that employ processes found in natural biological evolution. These algorithms search on a given population of potential solutions to find those that pass some specifications or criteria. In this paper, we apply modified genetic algorithm methodology for finding near-optimal solutions for TSP problem using shared neighbours to insure that the closest cities to have the highest priorities to be carried out to the next generation.
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@Article{JICS-8-055, author = {Mouhammd Al kasassbeh, A. Alabadleh and T. Al-Ramadeen}, title = {Shared Crossover Method for Solving Traveling Salesman Problem}, journal = {Journal of Information and Computing Science}, year = {2024}, volume = {8}, number = {1}, pages = {055--062}, abstract = {Genetic algorithms (GA) are evolutionary techniques that used crossover and mutation operators to solve optimization problems using a survival of the fittest idea. They have been used successfully in a variety of different problems, including the traveling salesman problem. The main idea of Traveling Salesman Problem (TSP) is to find the minimum traveling cost for visiting cities; the salesman must visit each city exactly once and return to the starting point of origin. Genetic algorithms are search methods that employ processes found in natural biological evolution. These algorithms search on a given population of potential solutions to find those that pass some specifications or criteria. In this paper, we apply modified genetic algorithm methodology for finding near-optimal solutions for TSP problem using shared neighbours to insure that the closest cities to have the highest priorities to be carried out to the next generation. }, issn = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22628.html} }
TY - JOUR T1 - Shared Crossover Method for Solving Traveling Salesman Problem AU - Mouhammd Al kasassbeh, A. Alabadleh and T. Al-Ramadeen JO - Journal of Information and Computing Science VL - 1 SP - 055 EP - 062 PY - 2024 DA - 2024/01 SN - 8 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22628.html KW - Traveling Salesman Problem (TSP), Genetic Algorithm (GA), Order Crossover (OX), Swap Crossover, Shared Crossover. AB - Genetic algorithms (GA) are evolutionary techniques that used crossover and mutation operators to solve optimization problems using a survival of the fittest idea. They have been used successfully in a variety of different problems, including the traveling salesman problem. The main idea of Traveling Salesman Problem (TSP) is to find the minimum traveling cost for visiting cities; the salesman must visit each city exactly once and return to the starting point of origin. Genetic algorithms are search methods that employ processes found in natural biological evolution. These algorithms search on a given population of potential solutions to find those that pass some specifications or criteria. In this paper, we apply modified genetic algorithm methodology for finding near-optimal solutions for TSP problem using shared neighbours to insure that the closest cities to have the highest priorities to be carried out to the next generation.
Mouhammd Al kasassbeh, A. Alabadleh and T. Al-Ramadeen. (2024). Shared Crossover Method for Solving Traveling Salesman Problem. Journal of Information and Computing Science. 8 (1). 055-062. doi:
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