An evolutionary algorithm for feed-forward neural networks optimization

We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a sp...

Full description

Saved in:
Bibliographic Details
Main Authors: Safi, Youssef, Bouroumi, Abdelaziz
Format: Conference Proceeding
Language:English
Subjects:
artificial neural networks
Classification algorithms
evolutionary algorithms
evolutionary strategies
Glass
machine learning
optimization
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 480
container_issue
container_start_page 475
container_title
container_volume
creator Safi, Youssef
Bouroumi, Abdelaziz
description We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a specific problem. The performance of the proposed algorithm in solving classification and prediction problems is experimentally tested using five real-world benchmark datasets. The experimental results are analyzed and compared to those produced by two other methods using two measures of performance.
doi_str_mv 10.1109/ICoCS.2014.7060901
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_7060901</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>7060901</ieee_id><sourcerecordid>7060901</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-2288e352a4e43633474112a6e79e8c90b51730f47ba70cf03108c84acea1c7b13</originalsourceid><addsrcrecordid>eNotj8FOwzAQRM0BCVTyA3DxDyTs2k5sH6sISqVKHIBz5aQbMCRx5bhU8PUEtac3l3maYewWoUAEe7-uQ_1SCEBVaKjAAl6wzGqDSlurKmXMFcum6RMA0FbaKHHNVsuR03foD8mH0cUf7vr3EH36GHgXIu-Idvkcji7u-EiH6PoZ6Rji18TDPvnB_7r_6g277Fw_UXbmgr09PrzWT_nmebWul5vcoy5TLoQxJEvhFClZSam0QhSuIm3JtBaaErWETunGaWg7kAimNcq15LDVDcoFuzt5PRFt99EP8-jt-a78A8DhS44</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>An evolutionary algorithm for feed-forward neural networks optimization</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>Safi, Youssef ; Bouroumi, Abdelaziz</creator><creatorcontrib>Safi, Youssef ; Bouroumi, Abdelaziz</creatorcontrib><description>We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a specific problem. The performance of the proposed algorithm in solving classification and prediction problems is experimentally tested using five real-world benchmark datasets. The experimental results are analyzed and compared to those produced by two other methods using two measures of performance.</description><identifier>EISBN: 9781479946488</identifier><identifier>EISBN: 9781479946471</identifier><identifier>EISBN: 1479946478</identifier><identifier>EISBN: 1479946486</identifier><identifier>DOI: 10.1109/ICoCS.2014.7060901</identifier><language>eng</language><publisher>IEEE</publisher><subject>artificial neural networks ; Classification algorithms ; evolutionary algorithms ; evolutionary strategies ; Glass ; machine learning ; optimization ; Training</subject><ispartof>2014 Second World Conference on Complex Systems (WCCS), 2014, p.475-480</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/7060901$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/7060901$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Safi, Youssef</creatorcontrib><creatorcontrib>Bouroumi, Abdelaziz</creatorcontrib><title>An evolutionary algorithm for feed-forward neural networks optimization</title><title>2014 Second World Conference on Complex Systems (WCCS)</title><addtitle>ICoCS</addtitle><description>We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a specific problem. The performance of the proposed algorithm in solving classification and prediction problems is experimentally tested using five real-world benchmark datasets. The experimental results are analyzed and compared to those produced by two other methods using two measures of performance.</description><subject>artificial neural networks</subject><subject>Classification algorithms</subject><subject>evolutionary algorithms</subject><subject>evolutionary strategies</subject><subject>Glass</subject><subject>machine learning</subject><subject>optimization</subject><subject>Training</subject><isbn>9781479946488</isbn><isbn>9781479946471</isbn><isbn>1479946478</isbn><isbn>1479946486</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2014</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj8FOwzAQRM0BCVTyA3DxDyTs2k5sH6sISqVKHIBz5aQbMCRx5bhU8PUEtac3l3maYewWoUAEe7-uQ_1SCEBVaKjAAl6wzGqDSlurKmXMFcum6RMA0FbaKHHNVsuR03foD8mH0cUf7vr3EH36GHgXIu-Idvkcji7u-EiH6PoZ6Rji18TDPvnB_7r_6g277Fw_UXbmgr09PrzWT_nmebWul5vcoy5TLoQxJEvhFClZSam0QhSuIm3JtBaaErWETunGaWg7kAimNcq15LDVDcoFuzt5PRFt99EP8-jt-a78A8DhS44</recordid><startdate>201411</startdate><enddate>201411</enddate><creator>Safi, Youssef</creator><creator>Bouroumi, Abdelaziz</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201411</creationdate><title>An evolutionary algorithm for feed-forward neural networks optimization</title><author>Safi, Youssef ; Bouroumi, Abdelaziz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-2288e352a4e43633474112a6e79e8c90b51730f47ba70cf03108c84acea1c7b13</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2014</creationdate><topic>artificial neural networks</topic><topic>Classification algorithms</topic><topic>evolutionary algorithms</topic><topic>evolutionary strategies</topic><topic>Glass</topic><topic>machine learning</topic><topic>optimization</topic><topic>Training</topic><toplevel>online_resources</toplevel><creatorcontrib>Safi, Youssef</creatorcontrib><creatorcontrib>Bouroumi, Abdelaziz</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 Electronic Library (IEL)</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>Safi, Youssef</au><au>Bouroumi, Abdelaziz</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>An evolutionary algorithm for feed-forward neural networks optimization</atitle><btitle>2014 Second World Conference on Complex Systems (WCCS)</btitle><stitle>ICoCS</stitle><date>2014-11</date><risdate>2014</risdate><spage>475</spage><epage>480</epage><pages>475-480</pages><eisbn>9781479946488</eisbn><eisbn>9781479946471</eisbn><eisbn>1479946478</eisbn><eisbn>1479946486</eisbn><abstract>We propose an evolutionary algorithm for optimizing both the topology and the synaptic weights of single hidden-layer feed-forward neural networks (SLFNs). We introduce new evolutionary operators of recombination and mutation we designed for evolving a population of SLFNs candidate solutions to a specific problem. The performance of the proposed algorithm in solving classification and prediction problems is experimentally tested using five real-world benchmark datasets. The experimental results are analyzed and compared to those produced by two other methods using two measures of performance.</abstract><pub>IEEE</pub><doi>10.1109/ICoCS.2014.7060901</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISBN: 9781479946488
ispartof 2014 Second World Conference on Complex Systems (WCCS), 2014, p.475-480
issn
language eng
recordid cdi_ieee_primary_7060901
source IEEE Electronic Library (IEL) Conference Proceedings
subjects artificial neural networks
Classification algorithms
evolutionary algorithms
evolutionary strategies
Glass
machine learning
optimization
Training
title An evolutionary algorithm for feed-forward neural networks optimization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T03%3A55%3A44IST&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=An%20evolutionary%20algorithm%20for%20feed-forward%20neural%20networks%20optimization&rft.btitle=2014%20Second%20World%20Conference%20on%20Complex%20Systems%20(WCCS)&rft.au=Safi,%20Youssef&rft.date=2014-11&rft.spage=475&rft.epage=480&rft.pages=475-480&rft_id=info:doi/10.1109/ICoCS.2014.7060901&rft.eisbn=9781479946488&rft.eisbn_list=9781479946471&rft.eisbn_list=1479946478&rft.eisbn_list=1479946486&rft_dat=%3Cieee_6IE%3E7060901%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-2288e352a4e43633474112a6e79e8c90b51730f47ba70cf03108c84acea1c7b13%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=7060901&rfr_iscdi=true