Automatic knot placement by a genetic algorithm for data fitting with a spline

In order to obtain a good spline model from many measurement data, frequently we have to deal with bets as variables. Then the problem to be solved becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain a global optimum. We p...

Full description

Saved in:
Bibliographic Details
Main Authors: Yoshimoto, F., Moriyama, M., Harada, T.
Format: Conference Proceeding
Language:English
Subjects:
Genetic algorithms
Spline
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 169
container_issue
container_start_page 162
container_title
container_volume
creator Yoshimoto, F.
Moriyama, M.
Harada, T.
description In order to obtain a good spline model from many measurement data, frequently we have to deal with bets as variables. Then the problem to be solved becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain a global optimum. We propose a new method to convert the original problem into a discrete combinatorial optimization problem and solve the converted problem by a genetic algorithm. We construct individuals by considering candidates of the locations of knots as genes, and convert the continuous problem into a discrete problem. We search for the best model among the candidate models by using H. Akaike's (1974) Information Criterion (AIC). Our method can determine appropriate number and locations of knots automatically and simultaneously. We don't need any subjective parameters such as error tolerance or a smoothing factor, and good initial location of knots for iterative search. Numerical examples are given to show the effectiveness of our method.
doi_str_mv 10.1109/SMA.1999.749336
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_749336</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>749336</ieee_id><sourcerecordid>749336</sourcerecordid><originalsourceid>FETCH-LOGICAL-i174t-4c06f47781388cb9fb7b376f50b958676fcd41efa66888379d63652783bb43dc3</originalsourceid><addsrcrecordid>eNotT0tLxDAYDIigrnsWPOUPtCZ-yZfkWBZfsOpBBW9LkiY12hdtRPbfW1nnMsPMMDCEXHBWcs7M1ctjVXJjTKmEAcAjcsYUGskYyvcTsp7nT7YADEouT8lT9Z2Hzubk6Vc_ZDq21ocu9Jm6PbW0CX34y2zbDFPKHx2Nw0Rrmy2NKefUN_RnsZfmPLapD-fkONp2Dut_XpG325vXzX2xfb572FTbInElciE8wyiU0hy09s5EpxwojJI5IzUuyteCh2gRtdagTI2A8lppcE5A7WFFLg-7KYSwG6fU2Wm_OzyGX9eiS50</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Automatic knot placement by a genetic algorithm for data fitting with a spline</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Yoshimoto, F. ; Moriyama, M. ; Harada, T.</creator><creatorcontrib>Yoshimoto, F. ; Moriyama, M. ; Harada, T.</creatorcontrib><description>In order to obtain a good spline model from many measurement data, frequently we have to deal with bets as variables. Then the problem to be solved becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain a global optimum. We propose a new method to convert the original problem into a discrete combinatorial optimization problem and solve the converted problem by a genetic algorithm. We construct individuals by considering candidates of the locations of knots as genes, and convert the continuous problem into a discrete problem. We search for the best model among the candidate models by using H. Akaike's (1974) Information Criterion (AIC). Our method can determine appropriate number and locations of knots automatically and simultaneously. We don't need any subjective parameters such as error tolerance or a smoothing factor, and good initial location of knots for iterative search. Numerical examples are given to show the effectiveness of our method.</description><identifier>ISBN: 076950065X</identifier><identifier>ISBN: 9780769500652</identifier><identifier>DOI: 10.1109/SMA.1999.749336</identifier><language>eng</language><publisher>IEEE</publisher><subject>Genetic algorithms ; Spline</subject><ispartof>Proceedings Shape Modeling International '99. International Conference on Shape Modeling and Applications, 1999, p.162-169</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/749336$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/749336$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Yoshimoto, F.</creatorcontrib><creatorcontrib>Moriyama, M.</creatorcontrib><creatorcontrib>Harada, T.</creatorcontrib><title>Automatic knot placement by a genetic algorithm for data fitting with a spline</title><title>Proceedings Shape Modeling International '99. International Conference on Shape Modeling and Applications</title><addtitle>SMA</addtitle><description>In order to obtain a good spline model from many measurement data, frequently we have to deal with bets as variables. Then the problem to be solved becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain a global optimum. We propose a new method to convert the original problem into a discrete combinatorial optimization problem and solve the converted problem by a genetic algorithm. We construct individuals by considering candidates of the locations of knots as genes, and convert the continuous problem into a discrete problem. We search for the best model among the candidate models by using H. Akaike's (1974) Information Criterion (AIC). Our method can determine appropriate number and locations of knots automatically and simultaneously. We don't need any subjective parameters such as error tolerance or a smoothing factor, and good initial location of knots for iterative search. Numerical examples are given to show the effectiveness of our method.</description><subject>Genetic algorithms</subject><subject>Spline</subject><isbn>076950065X</isbn><isbn>9780769500652</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1999</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotT0tLxDAYDIigrnsWPOUPtCZ-yZfkWBZfsOpBBW9LkiY12hdtRPbfW1nnMsPMMDCEXHBWcs7M1ctjVXJjTKmEAcAjcsYUGskYyvcTsp7nT7YADEouT8lT9Z2Hzubk6Vc_ZDq21ocu9Jm6PbW0CX34y2zbDFPKHx2Nw0Rrmy2NKefUN_RnsZfmPLapD-fkONp2Dut_XpG325vXzX2xfb572FTbInElciE8wyiU0hy09s5EpxwojJI5IzUuyteCh2gRtdagTI2A8lppcE5A7WFFLg-7KYSwG6fU2Wm_OzyGX9eiS50</recordid><startdate>1999</startdate><enddate>1999</enddate><creator>Yoshimoto, F.</creator><creator>Moriyama, M.</creator><creator>Harada, T.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1999</creationdate><title>Automatic knot placement by a genetic algorithm for data fitting with a spline</title><author>Yoshimoto, F. ; Moriyama, M. ; Harada, T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i174t-4c06f47781388cb9fb7b376f50b958676fcd41efa66888379d63652783bb43dc3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Genetic algorithms</topic><topic>Spline</topic><toplevel>online_resources</toplevel><creatorcontrib>Yoshimoto, F.</creatorcontrib><creatorcontrib>Moriyama, M.</creatorcontrib><creatorcontrib>Harada, T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Yoshimoto, F.</au><au>Moriyama, M.</au><au>Harada, T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Automatic knot placement by a genetic algorithm for data fitting with a spline</atitle><btitle>Proceedings Shape Modeling International '99. International Conference on Shape Modeling and Applications</btitle><stitle>SMA</stitle><date>1999</date><risdate>1999</risdate><spage>162</spage><epage>169</epage><pages>162-169</pages><isbn>076950065X</isbn><isbn>9780769500652</isbn><abstract>In order to obtain a good spline model from many measurement data, frequently we have to deal with bets as variables. Then the problem to be solved becomes a continuous nonlinear and multivariate optimization problem with many local optima. Therefore, it is difficult to obtain a global optimum. We propose a new method to convert the original problem into a discrete combinatorial optimization problem and solve the converted problem by a genetic algorithm. We construct individuals by considering candidates of the locations of knots as genes, and convert the continuous problem into a discrete problem. We search for the best model among the candidate models by using H. Akaike's (1974) Information Criterion (AIC). Our method can determine appropriate number and locations of knots automatically and simultaneously. We don't need any subjective parameters such as error tolerance or a smoothing factor, and good initial location of knots for iterative search. Numerical examples are given to show the effectiveness of our method.</abstract><pub>IEEE</pub><doi>10.1109/SMA.1999.749336</doi><tpages>8</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISBN: 076950065X
ispartof Proceedings Shape Modeling International '99. International Conference on Shape Modeling and Applications, 1999, p.162-169
issn
language eng
recordid cdi_ieee_primary_749336
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Genetic algorithms
Spline
title Automatic knot placement by a genetic algorithm for data fitting with a spline
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T15%3A02%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_6IE&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Automatic%20knot%20placement%20by%20a%20genetic%20algorithm%20for%20data%20fitting%20with%20a%20spline&rft.btitle=Proceedings%20Shape%20Modeling%20International%20'99.%20International%20Conference%20on%20Shape%20Modeling%20and%20Applications&rft.au=Yoshimoto,%20F.&rft.date=1999&rft.spage=162&rft.epage=169&rft.pages=162-169&rft.isbn=076950065X&rft.isbn_list=9780769500652&rft_id=info:doi/10.1109/SMA.1999.749336&rft_dat=%3Cieee_6IE%3E749336%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i174t-4c06f47781388cb9fb7b376f50b958676fcd41efa66888379d63652783bb43dc3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=749336&rfr_iscdi=true