In this lecture, we start by reviewing the basics our motivation to solve Engineering Design Optimization problems with evolutionary metaheuristics (a form of population-baed direct search approach). To prepare to introduce the Genetic Algorithm (GA), one of the most well-known Evolutionary Algorithms, we spend most of this lecture covering foundational topics from population and quantitative genetics that will give us the necessary vocabulary for discussing the GA. In particular, we introduce concepts of qualitative and quantitative traits, characters, phenotypes, genes, chromosomes, genomes, and genotypes. We also discuss the "GxE to P" relationship between genotype and phenotype and the connection between phenotype and fitness. We close with a discussion of the four forces of evolution (mutation, gene flow/migration, natural selection, and genetic drift). Next time, we will discuss the constant tension between natural selection and genetic drift (and mutation) and how to manage (and sometimes harness) this tension in an evolutionary metaheuristic.
Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/0llubbbjkxxconlia235z/IEE598-Lecture1C-2026-01-20-Population_Genetics_of_Evolutionary_Algorithms-Notes.pdf?rlkey=pyc5jxvcmbjfietop5jchgiyp&dl=0
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