In this paper, a review of the conceptual and practical aspects of the aiNet (Artificial Immune Network) family of algorithms will be provided. This family of algorithms started with the aiNet algorithm, proposed in 2002 for data clustering and, since then, several variations have been developed for data clustering, biclustering and optimization in general. Although the algorithms will be posit...

The traditional immune algorithm (IA) is based on a self–nonself biological immunity mechanism. Recently, a novel immune theory called the danger model theory has provided more suitable biological information for data handling compared with the self–nonself mechanism. According to the danger model theory and based on past experiences of the genetic and artificial IA, we present the Danger Model...

To reduce the transmissions among sensor nodes and prolong the lifecycle of the wireless sensor network, the immune theory and anycast technique were brought into the mobile agent routing mechanism, and an immune-based MA anycast routing algorithm was put forward. In this algorithm, the diversity and self-adaptation characters of artificial immune system were used to find out the optimal data s...

This paper presents a methodology that designs a fault detection Artificial Immune System (AIS) based on immune theory. The fault detection is a challenging problem due to increasing complexity of processes and agility necessary to avoid malfunction or accidents. The key fault detection challenge is determining the difference between normal and potential harmful activities. A promising solution...

Researchers in cognitive science struggle to find a computational model that best represents cognitive phenomena for the latter are extremely complex and their mechanisms are not well understood. The biological immune system, in turn, features intrinsic cognitive characteristics. As a consequence, computational models of the immune system are capable of naturally incorporating many of these abi...

Verifying the published results of algorithms is part of the usual research process. This helps to both validate the existing literature, but also quite often allows for new insights and augmentations of current systems in a methodological manner. This is very pertinent in emerging new areas such as Artificial Immune Systems, where it is essential that any algorithm is well understood and inves...

Verifying the published results of algorithms is part of the usual research process. This helps to both validate the existing literature, but also quite often allows for new insights and augmentations of current systems in a methodological manner. This is very pertinent in emerging new areas such as Artificial Immune Systems, where it is essential that any algorithm is well understood and inves...

González, Fabio Ph.D. The University of Memphis. May 2003. A Study of Artificial Immune Systems Applied to Anomaly Detection. Major Professor: Dipankar Dasgupta, Ph.D. The main goal of this research is to examine and to improve the anomaly detection function of artificial immune systems, specifically the negative selection algorithm and other self/non-self recognition techniques. This research ...

This paper revisits the Artificial Immmune Recognition System (AIRS) that has been developed as an immune-inspired supervised learning algorithm. Certain unnecessary complications of the original algorithm are discussed and means of overcomming these complexities are proposed. Experimental evidence is presented to support these revisions which do not sacrifice the accuracy of the original algor...