Loading…
Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems
In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up...
Saved in:
Published in: | Annals of mathematics and artificial intelligence 2013-08, Vol.68 (4), p.197-224 |
---|---|
Main Authors: | , , |
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-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883 |
---|---|
cites | cdi_FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883 |
container_end_page | 224 |
container_issue | 4 |
container_start_page | 197 |
container_title | Annals of mathematics and artificial intelligence |
container_volume | 68 |
creator | Sato, Hiroyuki Aguirre, Hernán Tanaka, Kiyoshi |
description | In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA
ϵ
+
and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with
items (bits) and
m
= {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for
m
≥ 4 objectives and that their effectiveness becomes larger as the number of objectives
m
increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed. |
doi_str_mv | 10.1007/s10472-012-9293-y |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1506379798</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2918193306</sourcerecordid><originalsourceid>FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883</originalsourceid><addsrcrecordid>eNp1kE9LwzAYh4MoOKcfwFvAiwerSZo2zXGM-Qcmuwyv4W2bjmxtOpN20G9vagVB8PQm5Pn93vAgdEvJIyVEPHlKuGARoSySTMbRcIZmNBFxJLgg5-E8vjDO40t05f2eECLTLJ0h-ABnIK819kcoNC7NSTtvuuEBF671vg1XDLbETd9BZ1qLjcXvm9UC-7Y-GbvDDdghavO9LrqQxQcLRw_FAR9dG2obf40uKqi9vvmZc7R9Xm2Xr9F68_K2XKyjgtOki0CXGSdaCFEQEWaSJ1rQlBUCQArIZFmyipVZCVpXklacsjTnAIIzIrMsnqP7qTbs_ey171RjfKHrGqxue69oQtJYSCFH9O4Pum97Z8PnFJM0ozKOAztHdKK-PThdqaMzDbhBUaJG52pyroJZNTpXQ8iwKeMDa3fa_Tb_H_oCOK2FfQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2918193306</pqid></control><display><type>article</type><title>Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems</title><source>Springer Nature</source><creator>Sato, Hiroyuki ; Aguirre, Hernán ; Tanaka, Kiyoshi</creator><creatorcontrib>Sato, Hiroyuki ; Aguirre, Hernán ; Tanaka, Kiyoshi</creatorcontrib><description>In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA
ϵ
+
and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with
items (bits) and
m
= {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for
m
≥ 4 objectives and that their effectiveness becomes larger as the number of objectives
m
increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.</description><identifier>ISSN: 1012-2443</identifier><identifier>EISSN: 1573-7470</identifier><identifier>DOI: 10.1007/s10472-012-9293-y</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Artificial Intelligence ; Complex Systems ; Computer Science ; Crossovers ; Effectiveness ; Genes ; Knapsack problem ; Mathematical analysis ; Mathematical models ; Mathematics ; Multiple objective analysis ; Mutation ; Mutations ; Operators ; Searching</subject><ispartof>Annals of mathematics and artificial intelligence, 2013-08, Vol.68 (4), p.197-224</ispartof><rights>Springer Science+Business Media B.V. 2012</rights><rights>Springer Science+Business Media B.V. 2012.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883</citedby><cites>FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883</cites></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>Sato, Hiroyuki</creatorcontrib><creatorcontrib>Aguirre, Hernán</creatorcontrib><creatorcontrib>Tanaka, Kiyoshi</creatorcontrib><title>Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems</title><title>Annals of mathematics and artificial intelligence</title><addtitle>Ann Math Artif Intell</addtitle><description>In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA
ϵ
+
and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with
items (bits) and
m
= {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for
m
≥ 4 objectives and that their effectiveness becomes larger as the number of objectives
m
increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.</description><subject>Artificial Intelligence</subject><subject>Complex Systems</subject><subject>Computer Science</subject><subject>Crossovers</subject><subject>Effectiveness</subject><subject>Genes</subject><subject>Knapsack problem</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mathematics</subject><subject>Multiple objective analysis</subject><subject>Mutation</subject><subject>Mutations</subject><subject>Operators</subject><subject>Searching</subject><issn>1012-2443</issn><issn>1573-7470</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><recordid>eNp1kE9LwzAYh4MoOKcfwFvAiwerSZo2zXGM-Qcmuwyv4W2bjmxtOpN20G9vagVB8PQm5Pn93vAgdEvJIyVEPHlKuGARoSySTMbRcIZmNBFxJLgg5-E8vjDO40t05f2eECLTLJ0h-ABnIK819kcoNC7NSTtvuuEBF671vg1XDLbETd9BZ1qLjcXvm9UC-7Y-GbvDDdghavO9LrqQxQcLRw_FAR9dG2obf40uKqi9vvmZc7R9Xm2Xr9F68_K2XKyjgtOki0CXGSdaCFEQEWaSJ1rQlBUCQArIZFmyipVZCVpXklacsjTnAIIzIrMsnqP7qTbs_ey171RjfKHrGqxue69oQtJYSCFH9O4Pum97Z8PnFJM0ozKOAztHdKK-PThdqaMzDbhBUaJG52pyroJZNTpXQ8iwKeMDa3fa_Tb_H_oCOK2FfQ</recordid><startdate>20130801</startdate><enddate>20130801</enddate><creator>Sato, Hiroyuki</creator><creator>Aguirre, Hernán</creator><creator>Tanaka, Kiyoshi</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>7SC</scope><scope>8FD</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>20130801</creationdate><title>Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems</title><author>Sato, Hiroyuki ; Aguirre, Hernán ; Tanaka, Kiyoshi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial Intelligence</topic><topic>Complex Systems</topic><topic>Computer Science</topic><topic>Crossovers</topic><topic>Effectiveness</topic><topic>Genes</topic><topic>Knapsack problem</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mathematics</topic><topic>Multiple objective analysis</topic><topic>Mutation</topic><topic>Mutations</topic><topic>Operators</topic><topic>Searching</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sato, Hiroyuki</creatorcontrib><creatorcontrib>Aguirre, Hernán</creatorcontrib><creatorcontrib>Tanaka, Kiyoshi</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</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>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</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>Annals of mathematics and artificial intelligence</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sato, Hiroyuki</au><au>Aguirre, Hernán</au><au>Tanaka, Kiyoshi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems</atitle><jtitle>Annals of mathematics and artificial intelligence</jtitle><stitle>Ann Math Artif Intell</stitle><date>2013-08-01</date><risdate>2013</risdate><volume>68</volume><issue>4</issue><spage>197</spage><epage>224</epage><pages>197-224</pages><issn>1012-2443</issn><eissn>1573-7470</eissn><abstract>In this work, we analyze variable space diversity of Pareto optimal solutions (POS) and study the effectiveness of crossover and mutation operators in evolutionary many-objective optimization. First we examine the diversity of variables in the true POS on many-objective 0/1 knapsack problems with up to 20 items (bits), showing that variables in POS become noticeably diverse as we increase the number of objectives. We also verify the effectiveness of conventional two-point and uniform crossovers, Local Recombination that selects mating parents based on proximity in objective space, and two-point and uniform crossover operators which Controls the maximum number of Crossed Genes (CCG). We use NSGA-II, SPEA2, IBEA
ϵ
+
and MSOPS, which adopt different selection methods, and many-objective 0/1 knapsack problems with
items (bits) and
m
= {2,4,6,8,10} objectives to verify the search performance of each crossover operator. Simulation results reveal that Local Recombination and CCG operators significantly improve search performance especially for NSGA-II and MSOPS, which have high diversity of genes in the population. Also, results show that CCG operators achieve higher search performance than Local Recombination for
m
≥ 4 objectives and that their effectiveness becomes larger as the number of objectives
m
increases. In addition, the contribution of CCG and mutation operators for the solutions search is analyzed and discussed.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s10472-012-9293-y</doi><tpages>28</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1012-2443 |
ispartof | Annals of mathematics and artificial intelligence, 2013-08, Vol.68 (4), p.197-224 |
issn | 1012-2443 1573-7470 |
language | eng |
recordid | cdi_proquest_miscellaneous_1506379798 |
source | Springer Nature |
subjects | Artificial Intelligence Complex Systems Computer Science Crossovers Effectiveness Genes Knapsack problem Mathematical analysis Mathematical models Mathematics Multiple objective analysis Mutation Mutations Operators Searching |
title | Variable space diversity, crossover and mutation in MOEA solving many-objective knapsack problems |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-12T21%3A18%3A41IST&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=Variable%20space%20diversity,%20crossover%20and%20mutation%20in%20MOEA%20solving%20many-objective%20knapsack%20problems&rft.jtitle=Annals%20of%20mathematics%20and%20artificial%20intelligence&rft.au=Sato,%20Hiroyuki&rft.date=2013-08-01&rft.volume=68&rft.issue=4&rft.spage=197&rft.epage=224&rft.pages=197-224&rft.issn=1012-2443&rft.eissn=1573-7470&rft_id=info:doi/10.1007/s10472-012-9293-y&rft_dat=%3Cproquest_cross%3E2918193306%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c415t-aed840e777c070e75b5e7162c7aa97a89dd2f2d8daeef91f4126b4aa74209883%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2918193306&rft_id=info:pmid/&rfr_iscdi=true |