Artificial Immune System (AIS) is a common name for a set of computational methods inspired by mechanisms underlying
vertebrate immune systems. Such systems are typically characterized by memory and ability to learn. Core concepts of AIS include:
An object being natural part of a system, pattern acceptable in this system
An object which recognizes objects different then own cells, not desired patterns
An object which stimulates antibodies
The most natural application of systems inspired by immune-based algorithms is security, for example in NIDS (network intrusion detection systems).
AIS are also used for machine learning (e.g. anti-spam filters), data analysis (unattended clustering) or agent systems (e.g. intelligent buildings).
This algorithm allows to generate a set of antibodies - detectors which are not stimulated by own cells.
Let D be an empty set of detectors
Repeat until not enough detectors have been generated
Create random detector
Check (using some metric) distance between detector and each own cell
If minimal distance is higher then given threshold, then add detector to set D
This algorithm allows to promote efficient detectors.
Let D be a set of detectors
Let S be a set of antigens to detect
For each antigen from S, choose N least distant (according to some metrics) detectors
For each such an antigen, create a number of clones inversely proportional to distance
Mutate each clone proportionally to its parent distance
Add clones to the D set
Repeat until stop condition (e.g. every antigen has been recognized)
The demonstration presents described algorithms sequentially.
Initially, the set of detectors is created with negative selection algorithm.
Afterwards, users can 'infect' the system with antigens.
Clonal selection algorithm is used for removing the selected infection.
The demonstration also shows which own cells would be false positives for mutated detectors.
In reality, such own cells would not be false positives – in a healthy organism, negative selection would not allow creation of detectors that would recognize its own cells, but some antigens would be incorrectly considered as own cells.
Number of own cells
Number of antibodies to create
Number of antibodies activated by single antigen
Number of clones created for activated antibody
Simulation step time
Objects in the demonstrations are points in 2 dimensional space, with Euclidean metric determining the distance.
Meaning of colors
Each element of the immune system is represented as a circle.
Antibody activated by antigen
New, mutated antibody