Sign inSubmit
computersciencejournal.org is not ours. This journal is published only at thescipub.com/jcs. Report a suspicious emailcomputersciencejournal.org is not ours · Report
ArticleMetrics
Research Article · Free to read · Published 31 October 2008

The Traveling Salesman Problem as a Benchmark Test for a Social-Based Genetic Algorithm

N. A. AL-Madi & A. T. Khader

Journal of Computer Science, Volume 4, Issue 10, pp. 871–876 · doi.org/10.3844/jcssp.2008.871.876

6,843 accesses4,514 downloads1 citations (Crossref)Metrics
Cite Check for updates

Abstract

Present research, showed Traveling Salesman Problem (TSP) to compare values and results concerning the averages and best fits of both, Simple Standard Genetic Algorithm (SGA) and a new approach of Genetic Algorithms named Social-Based Genetic Algorithm (SBGA). Results from TSP test on Social-Based Genetic Algorithms (SBGA) were presented. Results were encouraging in Social-Based Genetic Algorithms (SBGA) performs better in finding best fit solutions of generations in different populations than the Simple Standard Genetic Algorithm.

Licence and reuse

Free to read. This article carries the publisher’s copyright, as printed on it: © 2008 Science Publications. It is free to read; ask us before reusing it.

How to reuse this article

Responses (0)

Have a comment on this research? Sign in to respond. Responses are moderated and published with your name and affiliation; the authors are invited to reply. How we use your dataWrite a response

About this article

Cite this article

AL-Madi, N. A. & Khader, A. T. (2008). The Traveling Salesman Problem as a Benchmark Test for a Social-Based Genetic Algorithm. Journal of Computer Science, 4(10), 871–876. https://doi.org/10.3844/jcssp.2008.871.876

Licence
© Science Publications
Issue
Volume 4, Issue 10
Policies
Editorial independenceUse of generative AIData availabilityAppeals and complaints
Version
Version of record
The Traveling Salesman Problem as a Benchmark Test for a Social-Based Genetic AlgorithmJournal of Computer Science · Volume 4, Issue 10 · pp. 871–876 · © Science PublicationsDownload