Artificial immune system

An artificial immune system (AIS) is a type of optimisation algorithm inspired by the principles and processes of the vertebrate immune system. The algorithms typically exploit the immune system's characteristics of learning and memory to solve a problem. They are coupled to artificial intelligence and closely related to genetic algorithms.

Processes simulated in AlS include pattern recognition, hypermutation and clonal selection for B cells, negative selection of T cells, affinity maturation and immune network theory.

This article covers the algorithmic implementation of these processes. For underlying biological terminology, refer to the natural immune system.

Pattern recognition
Antibody & antigen representation is commonly implemented by strings of attributes. Attributes may be binary, integer or real-valued, although in principle any ordinal attribute could be used. Matching is done on the grounds of a distance metric, e.g. Euclidean distance, Manhattan distance or Hamming distance.

Hypermutation
Clonal selection algorithms are commonly used for antibody hypermutation. This allows the attribute string to be improved (as measured by a fitness function) using mutation alone. However, researchers argue that this clonal selection algorithm is similar to the mutation-based genetic algorithm and evolutionary strategies.

History
AIS began in the mid 70's with Farmer, Packard and Perelson's (1986) and Bersini and Varela's papers on immune networks (1990). However, it was only in the mid-90's that AIS became a subject area in its own right. Forrest et al (on negative selection) began in 1994; and Dasgupta conducted extensive studies on Negative Selection Algorithms. Hunt and Cooke started the works on Immune Network models in 1995; Timmis and Neal continued this work and made some improvements. De Castro & Von Zuben's and Nicosia & Cutello's work (on clonal selection) became notable in 2002. The first book on Artificial Immune Systems was edited by Dasgupta in 1999.

New ideas, such as danger theory and algorithms inspired by the innate immune system, are also now being explored. Although some doubt that they are yet offering anything over and above existing AIS algorithms, this is hotly debated, and the debate is providing one the main driving forces for AIS development at the moment.

Originally AIS set out to find efficient abstrations of processes found in the immune system but, more recently, it is becoming interested in modelling the biological processes and in applying immune algorithms to bioinformatics  problems.