In this lecture, we wrap up our discussion of the basic Genetic Algorithm (GA) and its hyper-parameters. We focusing on differences in implementation of the population structure as well as different genetic operators for selection and recombination and mutation (although we will have to finish the last few bits of our mutation discussion in the next lecture). Throughout the lecture, we emphasize the different roles for selection (and selective pressure), genetic drift, mutation, and even migration to some extent. This emphasizes the "evolution in a drift field" diagram that we introduced several lectures ago. Too much selective pressure can lead to premature convergence on local suboptima, and too little selective pressure can lead to solutions favored randomly due to genetic drift. Too much mutation can eliminate the possibility of fine-tuning solutions, and too little mutation is not enough of a guard against drift and selection purging all diversity from the population. Dynamically altering some of these parameters during the execution of a GA can provide a compromise among these tradeoffs.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/y3r7mum9hjcnr7elh4eww/IEE598-Lecture1F-2025-02-04-GA_Wrap_Up-Options_for_Selection_Crossover_Mutation_and_Extensions-Notes.pdf?rlkey=1uekaz5jwug5467yng4jlxgdi&dl=0
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