Revisiting Negative Selection Algorithms.
Saved in:
| Title: | Revisiting Negative Selection Algorithms. |
|---|---|
| Authors: | Zhou Ji1 zhou.ji@ieee.org, Dasgupta, Dipankar2 dasgupta@memphis.edu |
| Source: | Evolutionary Computation. Summer2007, Vol. 15 Issue 2, p223-251. 29p. |
| Subjects: | Algorithms, Selection theorems, Computer simulation of immune system, Machine learning, Computational learning theory |
| Abstract: | This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion. [ABSTRACT FROM AUTHOR] |
| Copyright of Evolutionary Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 25338858 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Revisiting Negative Selection Algorithms. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou+Ji%22">Zhou Ji</searchLink><relatesTo>1</relatesTo><i> zhou.ji@ieee.org</i><br /><searchLink fieldCode="AR" term="%22Dasgupta%2C+Dipankar%22">Dasgupta, Dipankar</searchLink><relatesTo>2</relatesTo><i> dasgupta@memphis.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Evolutionary+Computation%22">Evolutionary Computation</searchLink>. Summer2007, Vol. 15 Issue 2, p223-251. 29p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Selection+theorems%22">Selection theorems</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation+of+immune+system%22">Computer simulation of immune system</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+learning+theory%22">Computational learning theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper reviews the progress of negative selection algorithms, an anomaly/change detection approach in Artificial Immune Systems (AIS). Following its initial model, we try to identify the fundamental characteristics of this family of algorithms and summarize their diversities. There exist various elements in this method, including data representation, coverage estimate, affinity measure, and matching rules, which are discussed for different variations. The various negative selection algorithms are categorized by different criteria as well. The relationship and possible combinations with other AIS or other machine learning methods are discussed. Prospective development and applicability of negative selection algorithms and their influence on related areas are then speculated based on the discussion. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Evolutionary Computation is the property of MIT Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=25338858 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1162/evco.2007.15.2.223 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 223 Subjects: – SubjectFull: Algorithms Type: general – SubjectFull: Selection theorems Type: general – SubjectFull: Computer simulation of immune system Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Computational learning theory Type: general Titles: – TitleFull: Revisiting Negative Selection Algorithms. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou Ji – PersonEntity: Name: NameFull: Dasgupta, Dipankar IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Summer2007 Type: published Y: 2007 Identifiers: – Type: issn-print Value: 10636560 Numbering: – Type: volume Value: 15 – Type: issue Value: 2 Titles: – TitleFull: Evolutionary Computation Type: main |
| ResultId | 1 |
