Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks

Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this drawback, object-oriented GP (OOGP) introduced a means of evolving programs with multiple behaviours which could be easily extended to state-bas...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014) s. 305 - 311
Hlavní autoři: Medland, Michael Richard, Harrison, Kyle Robert, Ombuki-Berman, Beatrice M.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2014
Témata:
biologically inspired algorithms
complex networks
Computational modeling
evolutionary computation
genetic programming
Genetics
object-orientation
Programming
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this drawback, object-oriented GP (OOGP) introduced a means of evolving programs with multiple behaviours which could be easily extended to state-based programs. However, the production of programs which allowed embedded knowledge and produced readable code was still not easily addressed using the OOGP methodology. Exemplified through the evolution of graph models for complex networks, this paper demonstrates the benefits of a new approach to OOGP inspired by abstract classes and linear GP. Furthermore, the new approach to OOGP, named LinkableGP, facilitates the embedding of expert knowledge while also maintaining the benefits of OOGP.
AbstractList Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this drawback, object-oriented GP (OOGP) introduced a means of evolving programs with multiple behaviours which could be easily extended to state-based programs. However, the production of programs which allowed embedded knowledge and produced readable code was still not easily addressed using the OOGP methodology. Exemplified through the evolution of graph models for complex networks, this paper demonstrates the benefits of a new approach to OOGP inspired by abstract classes and linear GP. Furthermore, the new approach to OOGP, named LinkableGP, facilitates the embedding of expert knowledge while also maintaining the benefits of OOGP.
Author Medland, Michael Richard
Harrison, Kyle Robert
Ombuki-Berman, Beatrice M.
Author_xml – sequence: 1
  givenname: Michael Richard
  surname: Medland
  fullname: Medland, Michael Richard
  email: mm08sj@brocku.ca
  organization: Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
– sequence: 2
  givenname: Kyle Robert
  surname: Harrison
  fullname: Harrison, Kyle Robert
  email: kh08uh@brocku.ca
  organization: Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
– sequence: 3
  givenname: Beatrice M.
  surname: Ombuki-Berman
  fullname: Ombuki-Berman, Beatrice M.
  email: bombuki@brocku.ca
  organization: Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
BookMark eNot0EFOwzAQBVAjwQJKLwAbXyAhjp04s4QApVIFm-4rxxmnhsSOHEPh9qRQaaS_-Hp_MVfk3HmHhNywLGUsg7tX9bCu0zxjIi0hZxWUZ2QJsmJCAhTAZX5JPh9x8G6KQUXrOhr3SEd_wEC9ob55Rx0THyy6iC3t0GG0mo7Bd0ENwxF8WfWHrDMY0Gk8wrkd93TwLfYTNT5Q7Yexx286-4MPH9M1uTCqn3B5ygXZPj9t65dk87Za1_ebxEIWEyy4hKaYzwBwZoQSvNCAjDXAS4k5KsZ5q0uUOhdtwUXFNINCGINVVbZ8QW7_Zy0i7sZgBxV-dqdf8F8h9Vsl
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/NaBIC.2014.6921896
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
EISBN 9781479959372
1479959375
9781479959365
1479959367
EndPage 311
ExternalDocumentID 6921896
Genre orig-research
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i90t-e5379b59b5f9931f4a435c9e11b9367e2ea133dc6e7c24d53481c1954ffe886d3
IEDL.DBID RIE
IngestDate Thu Jun 29 18:36:48 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i90t-e5379b59b5f9931f4a435c9e11b9367e2ea133dc6e7c24d53481c1954ffe886d3
PageCount 7
ParticipantIDs ieee_primary_6921896
PublicationCentury 2000
PublicationDate 2014-July
PublicationDateYYYYMMDD 2014-07-01
PublicationDate_xml – month: 07
  year: 2014
  text: 2014-July
PublicationDecade 2010
PublicationTitle 2014 Sixth World Congress on Nature and Biologically Inspired Computing (NaBIC 2014)
PublicationTitleAbbrev NaBIC
PublicationYear 2014
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.5632302
Snippet Traditionally, GP used a single tree-based representation which does not lend itself well to state-based programs or multiple behaviours. To alleviate this...
SourceID ieee
SourceType Publisher
StartPage 305
SubjectTerms biologically inspired algorithms
complex networks
Computational modeling
evolutionary computation
genetic programming
Genetics
object-orientation
Programming
Title Demonstrating the power of object-oriented genetic programming via the inference of graph models for complex networks
URI https://ieeexplore.ieee.org/document/6921896
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09a8MwEBVJ6NCpLUnpNxo6Vkls2Za1Nm1oF5MhQ7ZgS6fiIXbIF_35vZPdlEKXgrGN8SHQ2dw76b07xh7zCJS1BWaq4EBgxDcizZ0VaUG_pVEWT77ZhMqydLHQsw57OmphAMCTz2BIt34v39ZmT0tlo0RjQNJJl3WVShqt1rcOZqxHWf78PiGyVjRsX_zVMcUHjOnZ_4Y6Z4Mf5R2fHWPKBetA1Wf7F1gRjCNnVR8cIRtfU3MzXjteF7SQImoqV4zgkeP3QLJE3vKuVmRwKHNvVB6HQENfqZr7RjhbjsiVe3I5fPKqIYZvB2w-fZ1P3kTbLkGUerwTEEulixgPh5gjcFGOSMhoCIJCy0RBCDnmo9YkoEwY2ZgUuIbqvTkHaZpYecl6VV3BFeMO7BjNQxvjNZKyoO3EUGKq6SDIZXrN-jRjy3VTEGPZTtbN349v2Sk5peG43rHebrOHe3ZiDrtyu3nwXvwCkvijfQ
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NS8NAEF1qFfSk0orf7sGjafOd7NVqabGGHnrorSS7s5JDk9I2xZ_vzCZWBC9CSELIsLCTMG9235th7DH1IVIqw0wVNFgY8aUVp1pZcUa_pYwUnkyziShJ4vlcTFvsaa-FAQBDPoMe3Zq9fFXKipbK-qHAgCTCA3YY-L5r12qtbyWMLfpJ-jweEF3L7zWv_uqZYkLG8PR_g52x7o_2jk_3UeWctaDosOoFlgTkyF3FB0fQxlfU3oyXmpcZLaVYJRUsRvjI8YsgYSJvmFdLMtjlqTHK90OgoalVzU0rnA1H7MoNvRw-eVFTwzddNhu-zgYjq2mYYOXC3loQeJHIAjw0og5H-yliISnAcTLhhRG4kGJGqmQIkXR9FZAGV1LFN60hjkPlXbB2URZwybgGZaO5qwK8-p6X0Yai62GyqcFJvfiKdWjGFqu6JMaimazrvx8_sOPR7H2ymIyTtxt2Qg6qGa-3rL1dV3DHjuRum2_W98ajXz4ZpsQ
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2014+Sixth+World+Congress+on+Nature+and+Biologically+Inspired+Computing+%28NaBIC+2014%29&rft.atitle=Demonstrating+the+power+of+object-oriented+genetic+programming+via+the+inference+of+graph+models+for+complex+networks&rft.au=Medland%2C+Michael+Richard&rft.au=Harrison%2C+Kyle+Robert&rft.au=Ombuki-Berman%2C+Beatrice+M.&rft.date=2014-07-01&rft.pub=IEEE&rft.spage=305&rft.epage=311&rft_id=info:doi/10.1109%2FNaBIC.2014.6921896&rft.externalDocID=6921896