By Topic

Solving the traveling salesmen problem through genetic algorithm with new variation order crossover

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sharma, S. ; Comput. Sci. Dept., Banasthali Univ., Jaipur, India ; Gupta, K.

Genetic Algorithm (GAs) is used to solve optimization problems. It is depended on the selection operator, crossover and mutation rates. In this paper Roulette Wheel Selection (RWS) operator with different crossover & mutation probabilities, is used to solve well known optimization problem Traveling Salesmen Problem (TSP). We have compared the results of RWS with another selection method Stochastic Universal Selection(SUS), which demonstrate that the SUS is better for small number of cities; but as the number of cities increases RWS is far much better than SUS. We have also compared the results with a variation between mutation & crossover probability which concludes that mutation is more effective for decimal chromosome. We have proposed a new crossover operator which is variation of Order Crossover (OX) and found results are better than existing crossover operator.

Published in:

Emerging Trends in Networks and Computer Communications (ETNCC), 2011 International Conference on

Date of Conference:

22-24 April 2011