In this lecture, we start with a description of two major classes of Artificial Immune System strategies – negative selection and clonal selection – along with the biological processes in the acquired/adaptive/specific immune system in vertebrates that inspired these algorithms. We focus on how both approaches maintain useful diversity, and we frame clonal selection as a form of multi-modal optimization (which will be discussed in more detail in Unit 4). This allows us to pivot to multi-objective optimization. In the last section of the lecture, we start outlining fundamentals of thinking about systems with multiple competing objectives – focusing first on game theory and the concept of the Nash equilibrium. Next time, we will define Pareto efficiency and start to introduce classical algorithms for finding Pareto-efficient sets.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/p8ly7l88ouvyc7m60lq8v/IEE598-Lecture3A-2026-02-19-Multicriteria_Decision_Making_Pareto_Optimality_and_Intro_to_Multiobjective_Evolutionary_Algorithms_MOEAs-Notes.pdf?rlkey=yhb60lgihm2mv0w1eid1nxas1&dl=0
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