JavaScript is disabled for your browser. Some features of this site may not work without it.
| dc.contributor.author | Lwin, Hnin Thant
|
|
| dc.date.accessioned | 2019-07-12T04:33:31Z | |
| dc.date.available | 2019-07-12T04:33:31Z | |
| dc.date.issued | 2013-02-26 | |
| dc.identifier.uri | http://onlineresource.ucsy.edu.mm/handle/123456789/839 | |
| dc.description.abstract | The Travelling Salesman Problem (TSP) is one of the hardest and the most fundamental problems in Computer Science. Although several techniques have been used in the past to reduce the running time of TSP, Genetic algorithms can reduce the running times of NP-complete problems substantially and have the capability of being parallelized. MapReduce is a parallel programming paradigm currently use and Hadoop is one of the most popular MapReduce frameworks because its robust, well designed and scalable file system. In this paper we use a genetic algorithm and parallelizing it on MapReduce Hadoop framework to reduce the running time of Travelling Salesman Problem. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Eleventh International Conference On Computer Applications (ICCA 2013) | en_US |
| dc.title | Genetic algorithm for Travelling Salesman Problem using MapReduce | en_US |
| dc.type | Article | en_US |
