In this lecture, we review the fundamentals of evolutionary (population-based) metaheuristic optimization algorithms (for engineering design optimization, EDO). This allows us to introduce the evolutionary notions of selective pressure and drift, two forces that independently drive changes in the frequency of strategies in populations over generations. Before getting into the concrete details of the basic Genetic Algorithm (GA) next time, we used the rest of this lecture to introduce terminology from evolutionary biology and population genetics in life sciences. These terms (chromosomes, genes, alleles, phenotype, characters, traits, fitness) will often be used in discussions of evolutionary algorithms (EA) as well, and so it is useful to have some background in them.
Whiteboard notes for this lecture can be found at: https://www.dropbox.com/s/9z2lc4y0om6kwxy/IEE598-Lecture1C-2022-01-18-Population_Genetics_of_Evolutionary_Algorithms.pdf?dl=0
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