Comparative analysis of genetic crossover operators in knapsack problem

The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover...

Full description

Saved in:
Bibliographic Details
Published in:Journal of Applied Sciences and Environmental Management 2016-09, Vol.20 (3), p.593-593
Main Authors: Hakimi, D, Oyewola, D.O., Yahaya, Y, Bolarin, G
Format: Article
Language:English
Subjects:
Arithmetic
Branch & bound algorithms
Chromosomes
Crossover
Evolutionary Algorithm
Genetic Algorithm
Genetic algorithms
Heuristic
Intermediate
Parents & parenting
Problems
Citations: 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-c2111-b87a2bed5a98f3ab779f77b884bf4b0565cb13d5f87e35ae14d9a1853b45584b3
cites
container_end_page 593
container_issue 3
container_start_page 593
container_title Journal of Applied Sciences and Environmental Management
container_volume 20
creator Hakimi, D
Oyewola, D.O.
Yahaya, Y
Bolarin, G
description The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.
doi_str_mv 10.4314/jasem.v20i3.13
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_proquest_miscellaneous_1850773281</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_30326f6e1aa1496da5c36a8c1edc1e43</doaj_id><sourcerecordid>1850773281</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2111-b87a2bed5a98f3ab779f77b884bf4b0565cb13d5f87e35ae14d9a1853b45584b3</originalsourceid><addsrcrecordid>eNpdkcFLwzAUxosoOKdXzwUvXjaTvqRJjzJ0DgZe9Bxe2tfRrW1qsg3235ttIuLhkUf48eXL9yXJPWdTAVw8rTFQN91nrIEph4tkxDkvJhry7PLPfp3chLBmLFNCi1Eyn7luQI_bZk8p9tgeQhNSV6cr6mnblGnpXQhuTz51A0XO-ZA2fbrpcQhYbtLBO9tSd5tc1dgGuvs5x8nn68vH7G2yfJ8vZs_LSZlFCxOrFWaWKomFrgGtUkWtlNVa2FpYJnNZWg6VrLUikEhcVAVyLcEKKSME42Rx1q0crs3gmw79wThszOnC-ZVBH323ZIBBltc5cUQuirxCWUKOuuRUxREQtR7PWvEPXzsKW9M1oaS2xZ7cLpj4LlMKMs0j-vAPXbudj3EdKQFMFqpgkZqeqVNonupfg5yZY0fm1JE5dWQ4wDeI-IXW</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1843059790</pqid></control><display><type>article</type><title>Comparative analysis of genetic crossover operators in knapsack problem</title><source>Free Full-Text Journals in Chemistry</source><creator>Hakimi, D ; Oyewola, D.O. ; Yahaya, Y ; Bolarin, G</creator><creatorcontrib>Hakimi, D ; Oyewola, D.O. ; Yahaya, Y ; Bolarin, G</creatorcontrib><description>The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.</description><identifier>ISSN: 1119-8362</identifier><identifier>ISSN: 2659-1502</identifier><identifier>EISSN: 1119-8362</identifier><identifier>EISSN: 2659-1499</identifier><identifier>DOI: 10.4314/jasem.v20i3.13</identifier><language>eng</language><publisher>Port Harcourt: Dr. Michael Horsfall Jnr, University of Port Harcourt, Department of Pure and Industrial Chemistry</publisher><subject>Arithmetic ; Branch &amp; bound algorithms ; Chromosomes ; Crossover ; Evolutionary Algorithm ; Genetic Algorithm ; Genetic algorithms ; Heuristic ; Intermediate ; Parents &amp; parenting ; Problems</subject><ispartof>Journal of Applied Sciences and Environmental Management, 2016-09, Vol.20 (3), p.593-593</ispartof><rights>Copyright Dr. Michael Horsfall Jnr, University of Port Harcourt, Department of Pure and Industrial Chemistry Sep 2016</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2111-b87a2bed5a98f3ab779f77b884bf4b0565cb13d5f87e35ae14d9a1853b45584b3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Hakimi, D</creatorcontrib><creatorcontrib>Oyewola, D.O.</creatorcontrib><creatorcontrib>Yahaya, Y</creatorcontrib><creatorcontrib>Bolarin, G</creatorcontrib><title>Comparative analysis of genetic crossover operators in knapsack problem</title><title>Journal of Applied Sciences and Environmental Management</title><description>The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.</description><subject>Arithmetic</subject><subject>Branch &amp; bound algorithms</subject><subject>Chromosomes</subject><subject>Crossover</subject><subject>Evolutionary Algorithm</subject><subject>Genetic Algorithm</subject><subject>Genetic algorithms</subject><subject>Heuristic</subject><subject>Intermediate</subject><subject>Parents &amp; parenting</subject><subject>Problems</subject><issn>1119-8362</issn><issn>2659-1502</issn><issn>1119-8362</issn><issn>2659-1499</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNpdkcFLwzAUxosoOKdXzwUvXjaTvqRJjzJ0DgZe9Bxe2tfRrW1qsg3235ttIuLhkUf48eXL9yXJPWdTAVw8rTFQN91nrIEph4tkxDkvJhry7PLPfp3chLBmLFNCi1Eyn7luQI_bZk8p9tgeQhNSV6cr6mnblGnpXQhuTz51A0XO-ZA2fbrpcQhYbtLBO9tSd5tc1dgGuvs5x8nn68vH7G2yfJ8vZs_LSZlFCxOrFWaWKomFrgGtUkWtlNVa2FpYJnNZWg6VrLUikEhcVAVyLcEKKSME42Rx1q0crs3gmw79wThszOnC-ZVBH323ZIBBltc5cUQuirxCWUKOuuRUxREQtR7PWvEPXzsKW9M1oaS2xZ7cLpj4LlMKMs0j-vAPXbudj3EdKQFMFqpgkZqeqVNonupfg5yZY0fm1JE5dWQ4wDeI-IXW</recordid><startdate>20160901</startdate><enddate>20160901</enddate><creator>Hakimi, D</creator><creator>Oyewola, D.O.</creator><creator>Yahaya, Y</creator><creator>Bolarin, G</creator><general>Dr. Michael Horsfall Jnr, University of Port Harcourt, Department of Pure and Industrial Chemistry</general><general>Joint Coordination Centre of the World Bank assisted National Agricultural Research Programme (NARP)</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H97</scope><scope>HCIFZ</scope><scope>L.G</scope><scope>L6V</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>SOI</scope><scope>DOA</scope></search><sort><creationdate>20160901</creationdate><title>Comparative analysis of genetic crossover operators in knapsack problem</title><author>Hakimi, D ; Oyewola, D.O. ; Yahaya, Y ; Bolarin, G</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2111-b87a2bed5a98f3ab779f77b884bf4b0565cb13d5f87e35ae14d9a1853b45584b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Arithmetic</topic><topic>Branch &amp; bound algorithms</topic><topic>Chromosomes</topic><topic>Crossover</topic><topic>Evolutionary Algorithm</topic><topic>Genetic Algorithm</topic><topic>Genetic algorithms</topic><topic>Heuristic</topic><topic>Intermediate</topic><topic>Parents &amp; parenting</topic><topic>Problems</topic><toplevel>online_resources</toplevel><creatorcontrib>Hakimi, D</creatorcontrib><creatorcontrib>Oyewola, D.O.</creatorcontrib><creatorcontrib>Yahaya, Y</creatorcontrib><creatorcontrib>Bolarin, G</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Database (Proquest)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural &amp; Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Middle East &amp; Africa Database</collection><collection>ProQuest Central</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) 3: Aquatic Pollution &amp; Environmental Quality</collection><collection>SciTech Premium Collection</collection><collection>Aquatic Science &amp; Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Environmental Science Database</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>Environmental Science Collection</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Journal of Applied Sciences and Environmental Management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hakimi, D</au><au>Oyewola, D.O.</au><au>Yahaya, Y</au><au>Bolarin, G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparative analysis of genetic crossover operators in knapsack problem</atitle><jtitle>Journal of Applied Sciences and Environmental Management</jtitle><date>2016-09-01</date><risdate>2016</risdate><volume>20</volume><issue>3</issue><spage>593</spage><epage>593</epage><pages>593-593</pages><issn>1119-8362</issn><issn>2659-1502</issn><eissn>1119-8362</eissn><eissn>2659-1499</eissn><abstract>The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem.</abstract><cop>Port Harcourt</cop><pub>Dr. Michael Horsfall Jnr, University of Port Harcourt, Department of Pure and Industrial Chemistry</pub><doi>10.4314/jasem.v20i3.13</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1119-8362
ispartof Journal of Applied Sciences and Environmental Management, 2016-09, Vol.20 (3), p.593-593
issn 1119-8362
2659-1502
1119-8362
2659-1499
language eng
recordid cdi_proquest_miscellaneous_1850773281
source Free Full-Text Journals in Chemistry
subjects Arithmetic
Branch & bound algorithms
Chromosomes
Crossover
Evolutionary Algorithm
Genetic Algorithm
Genetic algorithms
Heuristic
Intermediate
Parents & parenting
Problems
title Comparative analysis of genetic crossover operators in knapsack problem
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-01T01%3A26%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparative%20analysis%20of%20genetic%20crossover%20operators%20in%20knapsack%20problem&rft.jtitle=Journal%20of%20Applied%20Sciences%20and%20Environmental%20Management&rft.au=Hakimi,%20D&rft.date=2016-09-01&rft.volume=20&rft.issue=3&rft.spage=593&rft.epage=593&rft.pages=593-593&rft.issn=1119-8362&rft.eissn=1119-8362&rft_id=info:doi/10.4314/jasem.v20i3.13&rft_dat=%3Cproquest_doaj_%3E1850773281%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c2111-b87a2bed5a98f3ab779f77b884bf4b0565cb13d5f87e35ae14d9a1853b45584b3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=1843059790&rft_id=info:pmid/&rfr_iscdi=true