Principles of Artificial Immune Systems
Artificial immune systems (AIS) are inspired by the remarkable capabilities of the biological immune system, which protects living organisms from harmful invaders.
The immune system achieves this through three fundamental mechanisms:
- Recognition: the immune system distinguishes between self and non-self, identifying foreign substances called
antigens; - Memory: the immune system remembers previous encounters with pathogens, enabling a faster and stronger response upon subsequent exposures;
- Diversity: the immune system maintains a wide variety of immune cells, allowing recognition and response to a vast range of potential threats.
These principles inspire artificial immune systems to detect anomalies and adapt to changing environments.
In artificial immune systems, key concepts from the biological immune system are mapped to computational analogs to solve complex problems:
Antigen: represents a data instance or pattern that needs to be detected, such as an anomaly or an intruder in a system.
Antibody: acts as a detector or model that identifies specific patterns or anomalies within the data, similar to how antibodies recognize antigens in biology.
Memory cell: functions as a stored detector that represents learned patterns, enabling quick recognition and response to previously encountered situations.
In computational terms, these immune system principles are harnessed to create algorithms capable of solving complex problems such as anomaly detection and optimization.
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How do the computational analogs of immune system principles work in practice?
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Artificial immune systems (AIS) are inspired by the remarkable capabilities of the biological immune system, which protects living organisms from harmful invaders.
The immune system achieves this through three fundamental mechanisms:
- Recognition: the immune system distinguishes between self and non-self, identifying foreign substances called
antigens; - Memory: the immune system remembers previous encounters with pathogens, enabling a faster and stronger response upon subsequent exposures;
- Diversity: the immune system maintains a wide variety of immune cells, allowing recognition and response to a vast range of potential threats.
These principles inspire artificial immune systems to detect anomalies and adapt to changing environments.
In artificial immune systems, key concepts from the biological immune system are mapped to computational analogs to solve complex problems:
Antigen: represents a data instance or pattern that needs to be detected, such as an anomaly or an intruder in a system.
Antibody: acts as a detector or model that identifies specific patterns or anomalies within the data, similar to how antibodies recognize antigens in biology.
Memory cell: functions as a stored detector that represents learned patterns, enabling quick recognition and response to previously encountered situations.
In computational terms, these immune system principles are harnessed to create algorithms capable of solving complex problems such as anomaly detection and optimization.
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