In this lecture, we cover popular niching/niche-preserving methods for multi-modal optimization (which are also used in multi-objective optimization), which allow for multi-modal optimization techniques to find as many local fitness peaks as possible while minimizing representation (and maintaining high selective pressure) within each peak. These niching methods include fitness sharing methods (including variations inspired by k-means clustering), clearing and crowding methods, and the popular restricted tournament selection (RTS). Throughout the lecture, the computational complexity of different methods is highlighted so that at the end of the lecture (in detail in the PDF notes linked below) an assessment of the costs and benefits of each can be compared.
Whiteboard notes for this lecture (which include a list of computational complexities that was not covered in detail during the lecture) are available at:
https://www.dropbox.com/scl/fi/gdtk02v4gy9boxgj0vdbf/IEE598-Lecture4C-2025-03-04-Niching_Methods_in_Multi-Modal_Optimization-Notes.pdf?rlkey=gvfnxo20j9m76d4hfgvyhluzm&dl=0
