👉 Immune computing is an innovative approach to machine learning inspired by the principles of the human immune system. It leverages concepts such as self-organization, diversity, and adaptation to solve complex problems, particularly those involving large datasets and intricate patterns. In this framework, artificial immune systems (AIS) are designed to mimic the body's immune responses—such as the ability to recognize and neutralize foreign invaders (antigens) through mechanisms like clonal selection, where specific immune cells proliferate to target antigens. Similarly, AIS can identify and adapt to new threats by generating diverse populations of "antibodies" (search strategies) that evolve over time to improve their effectiveness. This dynamic and self-regulating process enables AIS to learn, generalize, and respond to novel challenges in a manner analogous to how the immune system protects the body from infections and diseases.
