Shared Crossover Method for Solving Traveling Salesman Problem
Year: 2013
Journal of Information and Computing Science, Vol. 8 (2013), Iss. 1 : pp. 55–62
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.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22628
Journal of Information and Computing Science, Vol. 8 (2013), Iss. 1 : pp. 55–62
Published online: 2013-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 8