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.