Computational Power of Killers and Helpers in the Immune System

Bibliographic Details
Main Author: Pacheco, José
Publication Date: 2004
Format: Master thesis
Language: por
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10451/13958
Summary: 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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2025-03-17T13:12:06Zoai:repositorio.ulisboa.pt:10455/3080Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T02:37:25.747002Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
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:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833601429958295552

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