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Article

Applicability issues of the real-valued negative selection algorithms

Authors:

Dipankar DasguptaAuthors Info & Claims

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

Pages 111 - 118

https://doi.org/10.1145/1143997.1144017

Published: 08 July 2006 Publication History

Abstract

The paper examines various applicability issues of the negative selection algorithms (NSA). Recently, concerns were raised on the use of NSAs, especially those using real-valued representation. In this paper, we argued that many reported issues are either due to improper usage of the method or general difficulties which are not specific to negative selection algorithms. On the contrary, the experiments with synthetic data and well-known real-world data show that NSAs have great flexibility to balance between efficiency and robustness, and to accommodate domain-oriented elements in the method, e.g. various distance measures. It is to be noted that all methods are not suitable for all datasets and data representation plays a major role.

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Cited By

Gupta KDasgupta D(2022)Negative Selection Algorithm Research and Applications in the Last Decade: A ReviewIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.31146613:2(110-128)Online publication date: Apr-2022
https://doi.org/10.1109/TAI.2021.3114661
Wang DLiang YYang X(2022)NKA: a pathogen dose-based natural killer cell algorithm and its application to classificationThe Journal of Supercomputing10.1007/s11227-021-04133-478:5(7016-7037)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04133-4
Jagriti Lobiyal D(2022)An efficient self-organized traffic maintenance scheme employing positive selection algorithmMultimedia Tools and Applications10.1007/s11042-022-13174-781:23(33107-33125)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13174-7
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Index Terms

Applicability issues of the real-valued negative selection algorithms
1. Computing methodologies
  1. Symbolic and algebraic manipulation

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cover image ACM Conferences

GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation

July 2006

2004 pages

ISBN:1595931864

DOI:10.1145/1143997

General Chair:
Mike Cattolico
Tiger Mountain Scientific, Inc., USA

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 July 2006

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GECCO06

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GECCO06: Genetic and Evolutionary Computation Conference

July 8 - 12, 2006

Washington, Seattle, USA

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GECCO '06 Paper Acceptance Rate 205 of 446 submissions, 46%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Gupta KDasgupta D(2022)Negative Selection Algorithm Research and Applications in the Last Decade: A ReviewIEEE Transactions on Artificial Intelligence10.1109/TAI.2021.31146613:2(110-128)Online publication date: Apr-2022
https://doi.org/10.1109/TAI.2021.3114661
Wang DLiang YYang X(2022)NKA: a pathogen dose-based natural killer cell algorithm and its application to classificationThe Journal of Supercomputing10.1007/s11227-021-04133-478:5(7016-7037)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11227-021-04133-4
Jagriti Lobiyal D(2022)An efficient self-organized traffic maintenance scheme employing positive selection algorithmMultimedia Tools and Applications10.1007/s11042-022-13174-781:23(33107-33125)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-13174-7
Singh KKaur LMaini R(2021)A survey of intrusion detection techniques based on negative selection algorithmInternational Journal of System Assurance Engineering and Management10.1007/s13198-021-01357-813:S1(175-185)Online publication date: 9-Nov-2021
https://doi.org/10.1007/s13198-021-01357-8
Luo WLiu RJiang HZhao DWu L(2018)Three Branches of Negative Representation of Information: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2018.28299072:6(411-425)Online publication date: Dec-2018
https://doi.org/10.1109/TETCI.2018.2829907
Timmis J(2018)Immune ComputingUnconventional Computing10.1007/978-1-4939-6883-1_282(503-518)Online publication date: 26-Aug-2018
https://doi.org/10.1007/978-1-4939-6883-1_282
Alizadeh EMeskin NKhorasani K(2017)A Negative Selection Immune System Inspired Methodology for Fault Diagnosis of Wind TurbinesIEEE Transactions on Cybernetics10.1109/TCYB.2016.258238447:11(3799-3813)Online publication date: Nov-2017
https://doi.org/10.1109/TCYB.2016.2582384
Saurabh PVerma B(2016)An efficient proactive artificial immune system based anomaly detection and prevention systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.03.04260:C(311-320)Online publication date: 30-Oct-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.03.042
Timmis J(2015)ImmunecomputingEncyclopedia of Complexity and Systems Science10.1007/978-3-642-27737-5_282-3(1-16)Online publication date: 22-Sep-2015
https://doi.org/10.1007/978-3-642-27737-5_282-3
Prasad JGhosh K(2014)Negative selection algorithm for monitoring processes with large number of variables2014 IEEE Conference on Control Applications (CCA)10.1109/CCA.2014.6981435(778-783)Online publication date: Oct-2014
https://doi.org/10.1109/CCA.2014.6981435
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