Skip to the content

Finding shortest path with learning algorithms

Bagheri, A, Akbarzadeh, M and Saraee, M 2008, 'Finding shortest path with learning algorithms' , International Journal of Artificial Intelligence, 1 (A08) .

Full text not available from this repository. (Request a copy)

Abstract

This paper presents an approach to the shortest path routing problem that uses one of the most popular learning algorithms. The Genetic Algorithm (GA) is one of the most powerful and successful method in stochastic search and optimization techniques based on the principles of the evolution theory. The crossover operation examines the current solutions in order to find better ones and the mutation operation introduces a new alternative route. The shortest path problem concentrates on finding the path with minimum distance, time or cost from a source node to the goal node. Routing decisions are based on constantly changing predictions of the weights. Finally we arrange some experiments to testify the efficiency of our method. In most of the experiments, the Genetic algorithms found the shortest path in a quick time and had good performance.

Item Type: Article
Themes: Media, Digital Technology and the Creative Economy
Schools: Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
Journal or Publication Title: International Journal of Artificial Intelligence
Publisher: Spring and Autumn
Refereed: Yes
ISSN: 0974-0635
Related URLs:
Depositing User: Dr Mo Saraee
Date Deposited: 21 Oct 2011 12:09
Last Modified: 20 Aug 2013 18:15
URI: http://usir.salford.ac.uk/id/eprint/18595

Actions (login required)

Edit record (repository staff only)

No Altmetrics available