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Article Open Access January 10, 2025

Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence

Praveen Kumar Myakala 1, Chiranjeevi Bura 1 and Anil Kumar Jonnalagadda 1
1
Independent Researchers, Dallas, Texas, USA
Publihed in: Current Research in Public Health (Volume 5, Issue 1, 2025)
Page(s): 1-13
DOI: 10.31586/jaibd.2025.1233
Received
December 02, 2024
Revised
January 05, 2025
Accepted
January 08, 2025
Published
January 10, 2025
Keywords
Artificial Immune Systems (AIS); Bio-inspired Computation; Computational Intelligence; Clonal Selection Algorithm; Anomaly Detection
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright: Copyright © The Author(s), 2025. Published by Scientific Publications
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Cite This Article

APA Style
Myakala, P. K. , Bura, C. , & Jonnalagadda, A. K. (2025). Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence. Current Research in Public Health, 5(1), 1-13. https://doi.org/10.31586/jaibd.2025.1233
ACS Style
Myakala, P. K. ; Bura, C. ; Jonnalagadda, A. K. Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence. Current Research in Public Health 2025 5(1), 1-13. https://doi.org/10.31586/jaibd.2025.1233
Chicago/Turabian Style
Myakala, Praveen Kumar, Chiranjeevi Bura, and Anil Kumar Jonnalagadda. 2025. "Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence". Current Research in Public Health 5, no. 1: 1-13. https://doi.org/10.31586/jaibd.2025.1233
AMA Style
Myakala PK, Bura C, Jonnalagadda AK. Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence. Current Research in Public Health. 2025; 5(1):1-13. https://doi.org/10.31586/jaibd.2025.1233 
@Article{crph1233,
AUTHOR = {Myakala, Praveen Kumar and Bura, Chiranjeevi and Jonnalagadda, Anil Kumar},
TITLE = {Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence},
JOURNAL = {Current Research in Public Health},
VOLUME = {5},
YEAR = {2025},
NUMBER = {1},
PAGES = {1-13},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1233},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2025.1233},
ABSTRACT = {Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.},
}
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AB  - Artificial Immune Systems (AIS) are bio-inspired computational frameworks that emulate the adaptive mechanisms of the human immune system, such as self/non-self discrimination, clonal selection, and immune memory. These systems have demonstrated significant potential in addressing complex challenges across optimization, anomaly detection, and adaptive system control. This paper provides a comprehensive exploration of AIS applications in domains such as cybersecurity, resource allocation, and autonomous systems, highlighting the growing importance of hybrid AIS models. Recent advancements, including integrations with machine learning, quantum computing, and bioinformatics, are discussed as solutions to scalability, high-dimensional data processing, and efficiency challenges. Core algorithms, such as the Negative Selection Algorithm (NSA) and Clonal Selection Algorithm (CSA), are examined, along with limitations in interpretability and compatibility with emerging AI paradigms. The paper concludes by proposing future research directions, emphasizing scalable hybrid frameworks, quantum-inspired approaches, and real-time adaptive systems, underscoring AIS's transformative potential across diverse computational fields.
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