Computational Power of Killers and Helpers in the Immune System

Detalhes bibliográficos
Autor(a) principal: Pacheco, José
Data de Publicação: 2004
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10451/13958
Resumo: The natural immune system is a subject of great research interest because of its powerful information processing capabilities. It uses characteristics such as learning, memory and associative retrieval to solve recognition and classification tasks. The model presented in this work belongs to the class of models introduced by Farmer et al and is inspired by the hypothesis of clonal selection theory and idiotypic network introduced by Niels Jerne. The main objective is to present a modified Immunological Algorithm that can be used in order to solve problems much in the way that Evolutionary Algorithms or certain types of Artificial Neural Networks do. Besides presenting the algorithm itself we discuss his various parameters, the way to present problems to it and how to extract results from its outcome. The model is then described as being a meta-algorithm to the Probabilistic Algorithms set. Several real problems are presented in order to compare this model with other types of Biologically inspired solving problems models. Finally we discuss various metrics to compare the efficiency and the results of the various biologically inspired models
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spelling Computational Power of Killers and Helpers in the Immune SystemArtificial immune systemsprobabilistic algorithmsThe natural immune system is a subject of great research interest because of its powerful information processing capabilities. It uses characteristics such as learning, memory and associative retrieval to solve recognition and classification tasks. The model presented in this work belongs to the class of models introduced by Farmer et al and is inspired by the hypothesis of clonal selection theory and idiotypic network introduced by Niels Jerne. The main objective is to present a modified Immunological Algorithm that can be used in order to solve problems much in the way that Evolutionary Algorithms or certain types of Artificial Neural Networks do. Besides presenting the algorithm itself we discuss his various parameters, the way to present problems to it and how to extract results from its outcome. The model is then described as being a meta-algorithm to the Probabilistic Algorithms set. Several real problems are presented in order to compare this model with other types of Biologically inspired solving problems models. Finally we discuss various metrics to compare the efficiency and the results of the various biologically inspired modelsDepartment of Informatics, University of LisbonCosta, José Félix Gomes daRepositório da Universidade de LisboaPacheco, José2009-02-10T13:12:43Z2004-072004-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/13958porinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T15:59:27Zoai:repositorio.ul.pt:10451/13958Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:35:52.580827Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Computational Power of Killers and Helpers in the Immune System
title Computational Power of Killers and Helpers in the Immune System
spellingShingle Computational Power of Killers and Helpers in the Immune System
Pacheco, José
Artificial immune systems
probabilistic algorithms
title_short Computational Power of Killers and Helpers in the Immune System
title_full Computational Power of Killers and Helpers in the Immune System
title_fullStr Computational Power of Killers and Helpers in the Immune System
title_full_unstemmed Computational Power of Killers and Helpers in the Immune System
title_sort Computational Power of Killers and Helpers in the Immune System
author Pacheco, José
author_facet Pacheco, José
author_role author
dc.contributor.none.fl_str_mv Costa, José Félix Gomes da
Repositório da Universidade de Lisboa
dc.contributor.author.fl_str_mv Pacheco, José
dc.subject.por.fl_str_mv Artificial immune systems
probabilistic algorithms
topic Artificial immune systems
probabilistic algorithms
description The natural immune system is a subject of great research interest because of its powerful information processing capabilities. It uses characteristics such as learning, memory and associative retrieval to solve recognition and classification tasks. The model presented in this work belongs to the class of models introduced by Farmer et al and is inspired by the hypothesis of clonal selection theory and idiotypic network introduced by Niels Jerne. The main objective is to present a modified Immunological Algorithm that can be used in order to solve problems much in the way that Evolutionary Algorithms or certain types of Artificial Neural Networks do. Besides presenting the algorithm itself we discuss his various parameters, the way to present problems to it and how to extract results from its outcome. The model is then described as being a meta-algorithm to the Probabilistic Algorithms set. Several real problems are presented in order to compare this model with other types of Biologically inspired solving problems models. Finally we discuss various metrics to compare the efficiency and the results of the various biologically inspired models
publishDate 2004
dc.date.none.fl_str_mv 2004-07
2004-07-01T00:00:00Z
2009-02-10T13:12:43Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10451/13958
url http://hdl.handle.net/10451/13958
dc.language.iso.fl_str_mv por
language por
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Department of Informatics, University of Lisbon
publisher.none.fl_str_mv Department of Informatics, University of Lisbon
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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