Adaptive hierarchical censored production rule-based system: A genetic algorithm approach
Autor(a) principal: | |
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Data de Publicação: | 1996 |
Outros Autores: | , |
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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Repositório Institucional da UNESP |
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2946 |
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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 |
|
_version_ |
1808128520154513408 |