Artificial Immune Systems: structure, function, diversity and an application to biclustering (original) (raw)
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Clonal vs . Negative Selection in Artificial Immune Systems ( AIS )
2016
In this paper, we review the bio-inspired Artificial Immune System (AIS) using two detection and selection mechanism known as negative selection and clonal selections. AIS mimic the behavior of natural immune system to find the unknown pattern that have not been seen by the system similar to what bodies would do in facing the microbial entities. We simulate the behavior of negative selection and clonal selection and compare them with each other to see the benefit of each one. Our goal is to design a system that can be utilized as an Intrusion Detection (ID) tool in networking security paradigms. General Terms Algorithms, Design, Security
Advances in artificial immune systems
IEEE Computational Intelligence Magazine, 2006
D uring the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI) as shown in .There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts-computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity.
An interdisciplinary perspective on artificial immune systems
Evolutionary Intelligence, 2008
... Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunology and engineering and ... such as mathematical and computational modelling of immunology, abstraction from those models into algorithm (and system) design and ...
Artificial Immune Systems (2010)
Arxiv preprint arXiv:1006.4949, 2010
The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault toler-ance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second genera-tion utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.
International Series in Operations Research & Management Science, 2010
The human immune system has numerous properties that make it ripe for exploitation in the computational domain, such as robustness and fault tolerance, and many different algorithms, collectively termed Artificial Immune Systems (AIS), have been inspired by it. Two generations of AIS are currently in use, with the first generation relying on simplified immune models and the second generation utilising interdisciplinary collaboration to develop a deeper understanding of the immune system and hence produce more complex models. Both generations of algorithms have been successfully applied to a variety of problems, including anomaly detection, pattern recognition, optimisation and robotics. In this chapter an overview of AIS is presented, its evolution is discussed, and it is shown that the diversification of the field is linked to the diversity of the immune system itself, leading to a number of algorithms as opposed to one archetypal system. Two case studies are also presented to help provide insight into the mechanisms of AIS; these are the idiotypic network approach and the Dendritic Cell Algorithm.
A Novel Combination of Negative and Positive Selection in Artificial Immune Systems
Artificial Immune System (AIS) is a multidisciplinary research area that combines the principles of immunology and computation. Negative Selection Algorithms (NSA) is one of the most popular models of AIS mainly designed for one-class learning problems such as anomaly detection. Positive Selection Algorithms (PSA) is the twin brother of NSA with quite similar performance for AIS. Both NSAs and PSAs comprise of two phases: generating a set D of detectors from a given set S of selves (detector generation phase); and then detecting if a given cell (new data instance) is self or non-self using the generated detector set (detection phase). In this paper, we propose a novel approach to combining NSAs and PSAs that employ binary representation and r-chunk matching rule. The new algorithm achieves smaller detector storage complexity and potentially better detection time in comparison with single NSAs or PSAs.
Artificial immune systems (AIS)-A new paradigm for heuristic decision making
2008
Over the last few years, more and more heuristic decision making techniques have been inspired by nature, e.g. evolutionary algorithms, ant colony optimisation and simulated annealing. More recently, a novel computational intelligence technique inspired by immunology has emerged, called Artificial Immune Systems (AIS). This immune system inspired technique has already been useful in solving some computational problems. In this keynote, we will very briefly describe the immune system metaphors that are relevant to AIS. We will then give some illustrative real-world problems suitable for AIS use and show a stepby-step algorithm walkthrough. A comparison of AIS to other well-known algorithms and areas for future work will round this keynote off. It should be noted that as AIS is still a young and evolving field, there is not yet a fixed algorithm template and hence actual implementations might differ somewhat from the examples given here.
Theoretical advances in artificial immune systems
2008
D uring the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI) as shown in .There has been increasing interest in the development of computational models inspired by several immunological principles. In particular, some are building models mimicking the mechanisms in the biological immune system (BIS) to better understand its natural processes and simulate its dynamical behavior in the presence of antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts-computational algorithms, techniques using simplified models of various immunological processes and functionalities. Like other biologically-inspired techniques, such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relatively young, the Artificial Immune System (AIS) is emerging as an active and attractive field involving models, techniques and applications of greater diversity.
Review of Artificial Immune System Research
2020
Artificial immune system (AIS) is a metaphorical computational intelligence system developed using ideas and theories extracted from biological immune system. It is a growing area of research attempts to bridge the divide between immunology and engineering, it exploits the mechanisms of the natural immune system including functions, principles and models in order to develop problem solving techniques. AIS is offering great diversity of problem solving algorithms and techniques. It is one of the attracting fields, which notably succeed in convincing researchers to start investigating and developing real-world models to non-linear engineering problems applied to different applications such as anomaly detection, classification, machine learning, clustering etc. In spite of those great properties of AIS, researchers continue arguing that AIS research does not yet reach the quality and importance of the other computational intelligence techniques like neural networks, DNA computation, ev...
Artificial Immune System: State of the Art Approach
International Journal of Computer Applications
The inspiration of framing the artificially developed immune system (AIS) is done through the biological immune system which compromise of signified information processing and self-adapting system. Since it originated in the 1990s, the branch of AIS gets a significant success in the field of Computational Intelligence. Present paper insights major works in the area of AIS and explore current advancements in applied system since past years. It has been observed that the particular research focused on three major considerable algorithms of AIS: (1) clonal selection algorithms (2) negative selection algorithm (3) artificial immune networks. However, computer scientists and engineers are motivated by the biological immune system to evolve new models and problem solving approaches. Developed AIS applications in extensive amount have received a lot of researcher's attention who were planning to establish models based on immune system and techniques in order to provide solutions for complicated problems of engineering. This paper presents a survey of current models of AIS and its algorithms.