Using a distance metric on genetic programs to understand genetic operators

I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the...

Full description

Saved in:
Bibliographic Details
Main Author: O'Reilly, U.-M.
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Genetic programming
Information analysis
Performance analysis
Sampling methods
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 4097 vol.5
container_issue
container_start_page 4092
container_title
container_volume 5
creator O'Reilly, U.-M.
description I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage further investigation into the use of edit distance.
doi_str_mv 10.1109/ICSMC.1997.637337
format conference_proceeding
fullrecord <record><control><sourceid>ieee_6IE</sourceid><recordid>TN_cdi_ieee_primary_637337</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>637337</ieee_id><sourcerecordid>637337</sourcerecordid><originalsourceid>FETCH-ieee_primary_6373373</originalsourceid><addsrcrecordid>eNp9jsGKwjAURR-jA1adD9BVfqD1JbGNXRdFGWalgjsJ9k3JME1KEhf-vYri0tW5cO7iAEw4ZpxjOdtU258q42WpskIqKdUHJCJXKuVFnvdgiGqBco655H1IOBYiLYU4DGAYwh-iwDlfJPC9D8Y2TLPahKjtiVhL0ZsTc5Y1ZCneZudd43UbWHTsbGvy92f90q4jr6PzYQyfv_o_0NeTI5iulrtqnRoiOnbetNpfjo9W-VZeARd9QDc</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Using a distance metric on genetic programs to understand genetic operators</title><source>IEEE Electronic Library (IEL) Conference Proceedings</source><creator>O'Reilly, U.-M.</creator><creatorcontrib>O'Reilly, U.-M.</creatorcontrib><description>I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage further investigation into the use of edit distance.</description><identifier>ISSN: 1062-922X</identifier><identifier>ISBN: 0780340531</identifier><identifier>ISBN: 9780780340534</identifier><identifier>EISSN: 2577-1655</identifier><identifier>DOI: 10.1109/ICSMC.1997.637337</identifier><language>eng</language><publisher>IEEE</publisher><subject>Artificial intelligence ; Genetic programming ; Information analysis ; Performance analysis ; Sampling methods</subject><ispartof>1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997, Vol.5, p.4092-4097 vol.5</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/637337$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54555,54920,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/637337$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>O'Reilly, U.-M.</creatorcontrib><title>Using a distance metric on genetic programs to understand genetic operators</title><title>1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation</title><addtitle>ICSMC</addtitle><description>I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage further investigation into the use of edit distance.</description><subject>Artificial intelligence</subject><subject>Genetic programming</subject><subject>Information analysis</subject><subject>Performance analysis</subject><subject>Sampling methods</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>0780340531</isbn><isbn>9780780340534</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1997</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNp9jsGKwjAURR-jA1adD9BVfqD1JbGNXRdFGWalgjsJ9k3JME1KEhf-vYri0tW5cO7iAEw4ZpxjOdtU258q42WpskIqKdUHJCJXKuVFnvdgiGqBco655H1IOBYiLYU4DGAYwh-iwDlfJPC9D8Y2TLPahKjtiVhL0ZsTc5Y1ZCneZudd43UbWHTsbGvy92f90q4jr6PzYQyfv_o_0NeTI5iulrtqnRoiOnbetNpfjo9W-VZeARd9QDc</recordid><startdate>1997</startdate><enddate>1997</enddate><creator>O'Reilly, U.-M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1997</creationdate><title>Using a distance metric on genetic programs to understand genetic operators</title><author>O'Reilly, U.-M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_6373373</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Artificial intelligence</topic><topic>Genetic programming</topic><topic>Information analysis</topic><topic>Performance analysis</topic><topic>Sampling methods</topic><toplevel>online_resources</toplevel><creatorcontrib>O'Reilly, U.-M.</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 Xplore / Electronic Library Online (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>O'Reilly, U.-M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Using a distance metric on genetic programs to understand genetic operators</atitle><btitle>1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation</btitle><stitle>ICSMC</stitle><date>1997</date><risdate>1997</risdate><volume>5</volume><spage>4092</spage><epage>4097 vol.5</epage><pages>4092-4097 vol.5</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>0780340531</isbn><isbn>9780780340534</isbn><abstract>I describe a distance metric called "edit" distance which quantifies the syntactic difference between two genetic programs. In the context of one specific problem, the 6 bit multiplexor, I use the metric to analyze the amount of new material introduced by different crossover operators, the difference among the best individuals of a population and the difference among the best individuals and the rest of the population. The relationships between these data and run performance are imprecise but they are sufficiently interesting to encourage further investigation into the use of edit distance.</abstract><pub>IEEE</pub><doi>10.1109/ICSMC.1997.637337</doi></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1062-922X
ispartof 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation, 1997, Vol.5, p.4092-4097 vol.5
issn 1062-922X
2577-1655
language eng
recordid cdi_ieee_primary_637337
source IEEE Electronic Library (IEL) Conference Proceedings
subjects Artificial intelligence
Genetic programming
Information analysis
Performance analysis
Sampling methods
title Using a distance metric on genetic programs to understand genetic operators
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T13%3A36%3A36IST&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=Using%20a%20distance%20metric%20on%20genetic%20programs%20to%20understand%20genetic%20operators&rft.btitle=1997%20IEEE%20International%20Conference%20on%20Systems,%20Man,%20and%20Cybernetics.%20Computational%20Cybernetics%20and%20Simulation&rft.au=O'Reilly,%20U.-M.&rft.date=1997&rft.volume=5&rft.spage=4092&rft.epage=4097%20vol.5&rft.pages=4092-4097%20vol.5&rft.issn=1062-922X&rft.eissn=2577-1655&rft.isbn=0780340531&rft.isbn_list=9780780340534&rft_id=info:doi/10.1109/ICSMC.1997.637337&rft_dat=%3Cieee_6IE%3E637337%3C/ieee_6IE%3E%3Cgrp_id%3Ecdi_FETCH-ieee_primary_6373373%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=637337&rfr_iscdi=true