The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

Immunity-based techniques are gaining popularity in wide area of applications, and emerging as a new branch of artificial intelligence (AI). The paper surveys the major works in this field during the last five years, in particular, it reviews the works of existing methods and the new initiatives.

Artificial immune system (AIS) research in the last five years

The field of Artificial Immune Systems (AIS) concerns the study and development of computationally interesting abstractions of the immune system. This survey tracks the development of AIS since its inception, and then attempts to make an assessment of its usefulness, defined in terms of 'distinctiveness' and 'effectiveness.' In this paper, the standard types of AIS are examined—Negative Selection, Clonal Selection and Immune Networks—as well as a new breed of AIS, based on the immunological 'danger theory.' The paper concludes that all types of AIS largely satisfy the criteria outlined for being useful, but only two types of AIS satisfy both criteria with any certainty.

How Do We Evaluate Artificial Immune Systems

This paper describes an artificial immune system algorithm which implements a fairly close analogue of the memory mechanism proposed by Jerne [1] (usually known as the Immune Network Theory). The algorithm demonstrates the ability of these types of network to produce meta-stable structures representing populated regions of the antigen space. The networks produced retain their structure indefinitely and capture inherent structure within the sets of antigens used to train them. Results from running the algorithm on a variety of data sets are presented and shown to be stable over long time periods and wide ranges of parameters. The potential of the algorithm as a tool for multivariate data analysis is also explored.

/pdf/meta-stable-memory-in-an-artificial-immune-network-j0t0amus6s.pdf

Meta-stable Memory in an Artificial Immune Network

This thesis examines the use of immunological metaphors in building serial, parallel, and distributed learning algorithms. It offers a basic study in the development of biologically-inspired algorithms which merge inspiration from biology with known, standard computing technology to examine robust methods of computing. This thesis begins by detailing key interactions found within the immune system that provide inspiration for the development of a learning system. It then exploits the use of more processing power for the development of faster algorithms. This leads to the exploration of distributed computing resources for the examination of more biologically plausible systems. This thesis offers the following main contributions. The components of the immune system that exhibit the capacity for learning are detailed. A framework for discussing learning algorithms is proposed. Three properties of every learning algorithm-memory, adaptation, and decision-making-are identified for this framework, and traditional learning algorithms are placed in the context of this framework. An investigation into the use of immunological components for learning is provided. This leads to an understanding of these components in terms of the learning framework. A simplification of the Artificial Immune Recognition System (AIRS) immune-inspired learning algorithm is provided by employing affinity-dependent somatic hypermutation. A parallel version of the Clonal Selection Algorithm (CLONALG) immune learning algorithm is developed. It is shown that basic parallel computing techniques can provide computational benefits for this algorithm. Exploring this technology further, a parallel version of AIRS is offered. It is shown that applying these same parallel computing techniques to AIRS, while less scalable than when applied to CLONALG, still provides computational gains. A distributed approach to AIRS is offered, and it is argued that this approach provides a more biologically appealing model. The simple distributed approach is proposed in terms of an initial step toward a more complex, distributed system. Biological immune systems exhibit complex cellular interactions. The mechanisms of these interactions, while often poorly understood, hint at an extremely powerful information processing/problem solving system. This thesis demonstrates how the use of immunological principles coupled with standard computing technology can lead to the development of robust, biologically inspired learning algorithms.

/pdf/exploiting-immunological-metaphors-in-the-development-of-3it4fgj5bq.pdf

Exploiting Immunological Metaphors in the Development of Serial, Parallel and Distributed Learning Algorithms

An investigation has been undertaken to repeat previous work on an artificial immune system for data analysis called AINE (Artificial Immune Network).The previous work was limited to testing the algorithm on relatively small data sets. The aim of this investigation is two fold,firstly to corroborate the results presented in previous work and secondly, to test the algorithm on a larger and more complex data set. A new re-implementation of AINE is then described and differences in behaviour are identified and explained. It is argued that the behaviourseen in the new implementation is more accurate than that seen in previous work and an in-depth analysis of the algorithm structure is undertaken in order to confirm theseobservations. The algorithm is also tested on new data and the results of this are presented. Comparisons are draw with other similar techniques for data mining and it is argued that AINE is an effective data-mining algorithm.

T. Knight

Papers

AINE: an immunological approach to data mining