Adaptive hierarchical censored production rule-based system: A genetic algorithm approach

Detalhes bibliográficos
Autor(a) principal: Bharadwaj, K. K. [UNESP]
Data de Publicação: 1996
Outros Autores: Hewahi, Nabil M., Brandao, Maria Augusta [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/3-540-61859-7_9
http://hdl.handle.net/11449/228077
Resumo: An adaptive system called GBHCPR (Genetic Based Hierarchical Censored Production Rule) system based on Hierarchical Censored Production Rule (HCPR) system is presented that relies on development of some ties between Genetic Based Machine Learning (GBML) and symbolic machine learning. Several genetic operators are suggested that include advanced genetic operators, namely, Fusion and Fission. An appropriate credit apportionment scheme is developed that supports both forwardand backward chaining of reasoning process. A scheme for credit revision during the operationsof the genetic operators Fusion and Fission is also presented. A prototype implementation is included and experimental results are presented to demonstrate the performance of the proposed system.
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spelling Adaptive hierarchical censored production rule-based system: A genetic algorithm approachGenetic algorithmHierarchical censored production rulesMachine learningAn adaptive system called GBHCPR (Genetic Based Hierarchical Censored Production Rule) system based on Hierarchical Censored Production Rule (HCPR) system is presented that relies on development of some ties between Genetic Based Machine Learning (GBML) and symbolic machine learning. Several genetic operators are suggested that include advanced genetic operators, namely, Fusion and Fission. An appropriate credit apportionment scheme is developed that supports both forwardand backward chaining of reasoning process. A scheme for credit revision during the operationsof the genetic operators Fusion and Fission is also presented. A prototype implementation is included and experimental results are presented to demonstrate the performance of the proposed system.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)DCCE / IBILCE / UNESPComputer Centre Ministery of Housing Biet HanounDCCE / IBILCE / UNESPCNPq: 301597/95-2Universidade Estadual Paulista (UNESP)Ministery of Housing Biet HanounBharadwaj, K. K. [UNESP]Hewahi, Nabil M.Brandao, Maria Augusta [UNESP]2022-04-29T07:26:34Z2022-04-29T07:26:34Z1996-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject81-90http://dx.doi.org/10.1007/3-540-61859-7_9Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 1159, p. 81-90.1611-33490302-9743http://hdl.handle.net/11449/22807710.1007/3-540-61859-7_92-s2.0-84948946531Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccess2022-04-29T07:26:34Zoai:repositorio.unesp.br:11449/228077Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T15:29:38.981701Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
title Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
spellingShingle Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
Bharadwaj, K. K. [UNESP]
Genetic algorithm
Hierarchical censored production rules
Machine learning
title_short Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
title_full Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
title_fullStr Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
title_full_unstemmed Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
title_sort Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
author Bharadwaj, K. K. [UNESP]
author_facet Bharadwaj, K. K. [UNESP]
Hewahi, Nabil M.
Brandao, Maria Augusta [UNESP]
author_role author
author2 Hewahi, Nabil M.
Brandao, Maria Augusta [UNESP]
author2_role author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Ministery of Housing Biet Hanoun
dc.contributor.author.fl_str_mv Bharadwaj, K. K. [UNESP]
Hewahi, Nabil M.
Brandao, Maria Augusta [UNESP]
dc.subject.por.fl_str_mv Genetic algorithm
Hierarchical censored production rules
Machine learning
topic Genetic algorithm
Hierarchical censored production rules
Machine learning
description An adaptive system called GBHCPR (Genetic Based Hierarchical Censored Production Rule) system based on Hierarchical Censored Production Rule (HCPR) system is presented that relies on development of some ties between Genetic Based Machine Learning (GBML) and symbolic machine learning. Several genetic operators are suggested that include advanced genetic operators, namely, Fusion and Fission. An appropriate credit apportionment scheme is developed that supports both forwardand backward chaining of reasoning process. A scheme for credit revision during the operationsof the genetic operators Fusion and Fission is also presented. A prototype implementation is included and experimental results are presented to demonstrate the performance of the proposed system.
publishDate 1996
dc.date.none.fl_str_mv 1996-01-01
2022-04-29T07:26:34Z
2022-04-29T07:26:34Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/3-540-61859-7_9
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 1159, p. 81-90.
1611-3349
0302-9743
http://hdl.handle.net/11449/228077
10.1007/3-540-61859-7_9
2-s2.0-84948946531
url http://dx.doi.org/10.1007/3-540-61859-7_9
http://hdl.handle.net/11449/228077
identifier_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 1159, p. 81-90.
1611-3349
0302-9743
10.1007/3-540-61859-7_9
2-s2.0-84948946531
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 81-90
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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