Loading…

Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system

Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the m...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematics and computation 2014-09, Vol.243, p.936-949
Main Authors: Umbarkar, A.J., Joshi, M.S., Hong, Wei-Chiang
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3
cites cdi_FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3
container_end_page 949
container_issue
container_start_page 936
container_title Applied mathematics and computation
container_volume 243
creator Umbarkar, A.J.
Joshi, M.S.
Hong, Wei-Chiang
description Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).
doi_str_mv 10.1016/j.amc.2014.06.033
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1567090821</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0096300314008698</els_id><sourcerecordid>1567090821</sourcerecordid><originalsourceid>FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3</originalsourceid><addsrcrecordid>eNp9kDFv2zAQhYmiAeqm-QHdODqD1KMokTI6GU7iFkgQD8lMsNSxpUGJLkkVSJf89dBx5kyHO7zv3d0j5CuDmgET3_a1Hk3dAGtrEDVw_oEsWC951Yl29ZEsAFai4gD8E_mc0h4ApGDtgjzfzT67_CeiHnCgOx219-jp1aw93YXD7HV2YaJbnDA7Q9f-d4hFP9Ll3e5qt11fUhsinScTppSjdlNxsaV9pcIhu9H9f7VItAzG47bKhIg0PaWM4xdyZrVPePFWz8njzfXD5kd1e7_9uVnfVoZzyJVsZC-GphzZ40pY7Dv41TIpAVBqhrxrrRGi0cZI0TMuGiFtJ1tspLUNB8vPyfLke4jh74wpq9Elg97rCcOcFOuEhBX0DStSdpKaGFKKaNUhulHHJ8VAHcNWe1XCVsewFQhVwi7M9xOD5Yd_DqNKxuFkcHARTVZDcO_QL70liO4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1567090821</pqid></control><display><type>article</type><title>Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system</title><source>Elsevier</source><source>Backfile Package - Mathematics (Legacy) [YMT]</source><source>ScienceDirect: Computer Science Backfile</source><creator>Umbarkar, A.J. ; Joshi, M.S. ; Hong, Wei-Chiang</creator><creatorcontrib>Umbarkar, A.J. ; Joshi, M.S. ; Hong, Wei-Chiang</creatorcontrib><description>Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).</description><identifier>ISSN: 0096-3003</identifier><identifier>EISSN: 1873-5649</identifier><identifier>DOI: 10.1016/j.amc.2014.06.033</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Convergence ; Diversity in GA ; DPGA ; Dual Population Genetic Algorithm ; Function optimization ; Genetic algorithms ; Mathematical analysis ; Mathematical models ; Optimization ; Parallel Dual-Population GA ; Premature convergence ; Reserves ; Serials ; Standard deviation</subject><ispartof>Applied mathematics and computation, 2014-09, Vol.243, p.936-949</ispartof><rights>2014 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3</citedby><cites>FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3</cites><orcidid>0000-0002-3001-2921</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0096300314008698$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3416,3551,27901,27902,45948,45978</link.rule.ids></links><search><creatorcontrib>Umbarkar, A.J.</creatorcontrib><creatorcontrib>Joshi, M.S.</creatorcontrib><creatorcontrib>Hong, Wei-Chiang</creatorcontrib><title>Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system</title><title>Applied mathematics and computation</title><description>Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).</description><subject>Convergence</subject><subject>Diversity in GA</subject><subject>DPGA</subject><subject>Dual Population Genetic Algorithm</subject><subject>Function optimization</subject><subject>Genetic algorithms</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Optimization</subject><subject>Parallel Dual-Population GA</subject><subject>Premature convergence</subject><subject>Reserves</subject><subject>Serials</subject><subject>Standard deviation</subject><issn>0096-3003</issn><issn>1873-5649</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kDFv2zAQhYmiAeqm-QHdODqD1KMokTI6GU7iFkgQD8lMsNSxpUGJLkkVSJf89dBx5kyHO7zv3d0j5CuDmgET3_a1Hk3dAGtrEDVw_oEsWC951Yl29ZEsAFai4gD8E_mc0h4ApGDtgjzfzT67_CeiHnCgOx219-jp1aw93YXD7HV2YaJbnDA7Q9f-d4hFP9Ll3e5qt11fUhsinScTppSjdlNxsaV9pcIhu9H9f7VItAzG47bKhIg0PaWM4xdyZrVPePFWz8njzfXD5kd1e7_9uVnfVoZzyJVsZC-GphzZ40pY7Dv41TIpAVBqhrxrrRGi0cZI0TMuGiFtJ1tspLUNB8vPyfLke4jh74wpq9Elg97rCcOcFOuEhBX0DStSdpKaGFKKaNUhulHHJ8VAHcNWe1XCVsewFQhVwi7M9xOD5Yd_DqNKxuFkcHARTVZDcO_QL70liO4</recordid><startdate>20140915</startdate><enddate>20140915</enddate><creator>Umbarkar, A.J.</creator><creator>Joshi, M.S.</creator><creator>Hong, Wei-Chiang</creator><general>Elsevier Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-3001-2921</orcidid></search><sort><creationdate>20140915</creationdate><title>Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system</title><author>Umbarkar, A.J. ; Joshi, M.S. ; Hong, Wei-Chiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Convergence</topic><topic>Diversity in GA</topic><topic>DPGA</topic><topic>Dual Population Genetic Algorithm</topic><topic>Function optimization</topic><topic>Genetic algorithms</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Optimization</topic><topic>Parallel Dual-Population GA</topic><topic>Premature convergence</topic><topic>Reserves</topic><topic>Serials</topic><topic>Standard deviation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Umbarkar, A.J.</creatorcontrib><creatorcontrib>Joshi, M.S.</creatorcontrib><creatorcontrib>Hong, Wei-Chiang</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts – Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Applied mathematics and computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Umbarkar, A.J.</au><au>Joshi, M.S.</au><au>Hong, Wei-Chiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system</atitle><jtitle>Applied mathematics and computation</jtitle><date>2014-09-15</date><risdate>2014</risdate><volume>243</volume><spage>936</spage><epage>949</epage><pages>936-949</pages><issn>0096-3003</issn><eissn>1873-5649</eissn><abstract>Various problems viz. population diversity problem, premature convergence problem and curse of dimensionality problem, are associated with Genetic Algorithm (GA). Dual Population GA (DPGA) helps to provide additional population diversity to the main population by means of crossbreeding between the main population and reserve population. This helps to solve the problem of premature convergence and helps in early convergence of the algorithm. The binary encoded Multithreaded Parallel DPGA (MPDPGA) is proposed in this paper to solve the problems of population diversity and premature convergence. The experimental results show that, the performance (mean, standard deviation and standard error of mean), student t-test, mean function evaluation and success rate of MPDPGA is better than serial DPGA (SDPGA) and simple GA (SGA).</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.amc.2014.06.033</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0002-3001-2921</orcidid></addata></record>
fulltext fulltext
identifier ISSN: 0096-3003
ispartof Applied mathematics and computation, 2014-09, Vol.243, p.936-949
issn 0096-3003
1873-5649
language eng
recordid cdi_proquest_miscellaneous_1567090821
source Elsevier; Backfile Package - Mathematics (Legacy) [YMT]; ScienceDirect: Computer Science Backfile
subjects Convergence
Diversity in GA
DPGA
Dual Population Genetic Algorithm
Function optimization
Genetic algorithms
Mathematical analysis
Mathematical models
Optimization
Parallel Dual-Population GA
Premature convergence
Reserves
Serials
Standard deviation
title Multithreaded Parallel Dual Population Genetic Algorithm (MPDPGA) for unconstrained function optimizations on multi-core system
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T21%3A35%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multithreaded%20Parallel%20Dual%20Population%20Genetic%20Algorithm%20(MPDPGA)%20for%20unconstrained%20function%20optimizations%20on%20multi-core%20system&rft.jtitle=Applied%20mathematics%20and%20computation&rft.au=Umbarkar,%20A.J.&rft.date=2014-09-15&rft.volume=243&rft.spage=936&rft.epage=949&rft.pages=936-949&rft.issn=0096-3003&rft.eissn=1873-5649&rft_id=info:doi/10.1016/j.amc.2014.06.033&rft_dat=%3Cproquest_cross%3E1567090821%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c330t-72786d2ead8e96fe850b417700e7a1e354fc662acc768136267f574e27ff230f3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1567090821&rft_id=info:pmid/&rfr_iscdi=true