New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem
Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experim...
Gespeichert in:
| Veröffentlicht in: | arXiv.org 2020-01 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | arXiv.org |
| container_volume | |
| creator | Pham, Dinh Thanh Huynh Thi Thanh Binh Bui Thu Lam |
| description | Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values. |
| doi_str_mv | 10.48550/arxiv.2001.11590 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2001_11590</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2350182992</sourcerecordid><originalsourceid>FETCH-LOGICAL-a522-f535c9842b2fc6eca44e1739c75b928ae0e079ea5305b9a80982627f8d9949f73</originalsourceid><addsrcrecordid>eNotkF1LwzAUhoMgOOZ-gFcGvO5MTpq1uZThFwy92X05jadbRpvUpKv67-2cV4f38PLy8DB2I8UyL7UW9xi_3bgEIeRSSm3EBZuBUjIrc4ArtkjpIISAVQFaqxnr3-iLd2T36F3qeGi4DV3tPA4ueG5jSCmMFHnoKeIQYuLO8x15Gpzl2O5CdMO-402IPIV2dH7Hhz3xIeJI7SklbCl16HkfQ91Sd80uG2wTLf7vnG2fHrfrl2zz_vy6fthkqAGyRittzYRcQ2NXZDHPSRbK2ELXBkokQaIwhFqJ6YGlMCWsoGjKD2Ny0xRqzm7Ps386qj66DuNPddJS_WmZGnfnxgT2eaQ0VIdwjH5iqkBpIUswBtQvap9nOw</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2350182992</pqid></control><display><type>article</type><title>New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Pham, Dinh Thanh ; Huynh Thi Thanh Binh ; Bui Thu Lam</creator><creatorcontrib>Pham, Dinh Thanh ; Huynh Thi Thanh Binh ; Bui Thu Lam</creatorcontrib><description>Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2001.11590</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Computer Science - Artificial Intelligence ; Computer Science - Neural and Evolutionary Computing ; Crossovers ; Genetic algorithms ; Operators ; Traveling salesman problem</subject><ispartof>arXiv.org, 2020-01</ispartof><rights>2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,782,786,887,27934</link.rule.ids><backlink>$$Uhttps://doi.org/10.48550/arXiv.2001.11590$$DView paper in arXiv$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.1007/978-3-319-11680-8_29$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink></links><search><creatorcontrib>Pham, Dinh Thanh</creatorcontrib><creatorcontrib>Huynh Thi Thanh Binh</creatorcontrib><creatorcontrib>Bui Thu Lam</creatorcontrib><title>New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem</title><title>arXiv.org</title><description>Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><subject>Crossovers</subject><subject>Genetic algorithms</subject><subject>Operators</subject><subject>Traveling salesman problem</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotkF1LwzAUhoMgOOZ-gFcGvO5MTpq1uZThFwy92X05jadbRpvUpKv67-2cV4f38PLy8DB2I8UyL7UW9xi_3bgEIeRSSm3EBZuBUjIrc4ArtkjpIISAVQFaqxnr3-iLd2T36F3qeGi4DV3tPA4ueG5jSCmMFHnoKeIQYuLO8x15Gpzl2O5CdMO-402IPIV2dH7Hhz3xIeJI7SklbCl16HkfQ91Sd80uG2wTLf7vnG2fHrfrl2zz_vy6fthkqAGyRittzYRcQ2NXZDHPSRbK2ELXBkokQaIwhFqJ6YGlMCWsoGjKD2Ny0xRqzm7Ps386qj66DuNPddJS_WmZGnfnxgT2eaQ0VIdwjH5iqkBpIUswBtQvap9nOw</recordid><startdate>20200114</startdate><enddate>20200114</enddate><creator>Pham, Dinh Thanh</creator><creator>Huynh Thi Thanh Binh</creator><creator>Bui Thu Lam</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20200114</creationdate><title>New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem</title><author>Pham, Dinh Thanh ; Huynh Thi Thanh Binh ; Bui Thu Lam</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a522-f535c9842b2fc6eca44e1739c75b928ae0e079ea5305b9a80982627f8d9949f73</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><topic>Crossovers</topic><topic>Genetic algorithms</topic><topic>Operators</topic><topic>Traveling salesman problem</topic><toplevel>online_resources</toplevel><creatorcontrib>Pham, Dinh Thanh</creatorcontrib><creatorcontrib>Huynh Thi Thanh Binh</creatorcontrib><creatorcontrib>Bui Thu Lam</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Computer Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pham, Dinh Thanh</au><au>Huynh Thi Thanh Binh</au><au>Bui Thu Lam</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem</atitle><jtitle>arXiv.org</jtitle><date>2020-01-14</date><risdate>2020</risdate><eissn>2331-8422</eissn><abstract>Traveling salesman problem (TSP) is a well-known in computing field. There are many researches to improve the genetic algorithm for solving TSP. In this paper, we propose two new crossover operators and new mechanism of combination crossover operators in genetic algorithm for solving TSP. We experimented on TSP instances from TSP-Lib and compared the results of proposed algorithm with genetic algorithm (GA), which used MSCX. Experimental results show that, our proposed algorithm is better than the GA using MSCX on the min, mean cost values.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2001.11590</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2020-01 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_arxiv_primary_2001_11590 |
| source | arXiv.org; Free E- Journals |
| subjects | Computer Science - Artificial Intelligence Computer Science - Neural and Evolutionary Computing Crossovers Genetic algorithms Operators Traveling salesman problem |
| title | New mechanism of combination crossover operators in genetic algorithm for solving the traveling salesman problem |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-01T06%3A01%3A01IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=New%20mechanism%20of%20combination%20crossover%20operators%20in%20genetic%20algorithm%20for%20solving%20the%20traveling%20salesman%20problem&rft.jtitle=arXiv.org&rft.au=Pham,%20Dinh%20Thanh&rft.date=2020-01-14&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2001.11590&rft_dat=%3Cproquest_arxiv%3E2350182992%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2350182992&rft_id=info:pmid/&rfr_iscdi=true |