In this lecture, we start by introducing distributed and parallel genetic algorithms (DGA/PGA) that not only have the potential to leverage parallel hardware resources but, perhaps surprisingly, can improve the performance of canonical genetic algorithms (GA's) through population-genetic effects that turn genetic drift in *meta-populations* into a source of diversity (instead of a force that reduces diversity, as it does in a single population). In particular, we introduce the idea of "shifting-balance theory" from Sewall Wright, which is one of the early models that attempts to understand the effect of limited gene flow on drift and selective pressure in complex population structures. This allows us to revisit the power and value of diversity in population-based evolutionary algorithms for optimization, and we use that to introduce the field of multi-modal optimization. Following this idea, we close with an introduction to niching methods for multi-modal optimization, starting with fitness sharing. We will pick up with this topic in our next lecture.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/dqfz3d2rienofebqpa30e/IEE598-Lecture4B-2025-02-25-From_DGA_PGA_to_Niching_Methods_for_Multi_Modal_Optimization-Notes.pdf?rlkey=fknh18uafdqh2ah2lqsfi7lcb&dl=0
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