Shared Crossover Method for Solving Traveling Salesman Problem
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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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