In this lecture, we review the Pareto perspective of multi-objective optimization, emphasizing Pareto efficiency and the Pareto front. We draw connections to a blend of population genetics/evolution as well as community ecology (related to niche spaces and co-existence). We introduce Age–Fitness Pareto Optimization (AFPO) as a motivational example, as it uses the diversity preserving aspects of multi-objective optimization to improve the evolvability of single-objective metaheuristics used in evolutionary robotics (although note that in the video, it is said that AFPO maximizes age/generations, but it actually minimizes age/generations; this is fixed in the notes linked below). From there, we describe several classical multi-objective genetic algorithm approaches that either explicitly incorporate weighted combinations of objectives or instead create sub-populations for different objectives and blend individuals from those subpopulations. This motivates the idea of Pareto ranking (a more modern approach to multi-objective evolutionary algorithms), which we will introduce (along with other diversity-preserving and diversity-enhancing mechanisms) next time.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/nwfytnxrlmqzvdoftdqtc/IEE598-Lecture3B-2025-02-20-Multi_Objective_Genetic_Algorithms-From_Weight_Based_Approaches_to_Pareto_Ranking-Notes.pdf?rlkey=pq81xpd5abfgikq6twsj73yys&dl=0
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