In this lecture, we introduce the field of "Immunocomputing", which studies information-processing mechanisms within the vertebrate immune system that help to maintain a diverse array of solutions to detecting and handling threats. Such dynamical systems must be have low false negative rates (i.e., detect as many threats as possible) while also having low false positive rates (i.e., avoid classifying artifacts of normal operations as being anomalous). We discuss the innate immune system, which is a highly conserved immune system to respond to general threats, and the adaptive/acquired immune system, which has immunological memory of past threats that it can use to deploy highly specific responses to those threats if found in the future. Particularly this second system has inspired Artificial Immune Systems (AIS), including negative selection (inspired by "central tolerance" in the thymus) and clonal selection. Both of these two algorithmic approaches form and maintain a diverse portfolio of threat/anomaly classifiers as opposed to clustering around a single good solution, as is often the case in Genetic Algorithms. This sets us up for our next unit on multi-criteria decision making, and so we close with a basic introduction to game theory/multi-objective optimization and Nash equilibria. We will introduce Pareto equilibria in the next lecture.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/ha6oe1rjwyzbwhnp4nsds/IEE598-Lecture2C-2025-02-13-Immunocomputing-Genetic_Approaches_for_Diverse_Solution_Portfolios-Notes.pdf?rlkey=qsljjcwafo03vrtoyc9u7de5d&dl=0
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