Application of Genetics Using Artificial Immune System Through Computation (original) (raw)

An Introduction to Artificial Immune Systems

Handbook of Natural Computing, 2012

The field of artificial immune systems (AIS) can be thought of comprising two threads of research: the employment of mathematical and computational techniques in the modelling of immunology, and the incorporation of immune system inspired mechanisms and metaphors in the development of engineering solutions. The former permits the integration of immunological data and sub-models into a coherent whole, which may be of value to immunologists in the facilitation of immunological understanding, hypothesis testing, and the direction of future research. The latter attempts harness the perceived properties of the immune system in the solving of engineering problems. This chapter concentrates on the latter: the development and application of immune inspiration to engineering solutions.

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.

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.

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.

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 (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.