Lecture 1E (2024-01-30): The Basic Genetic Algorithm and Its Implementation

In this lecture, we reveal the basic architecture of the simple GA. We then start to introduce three common selection operators (fitness-proportionate selection, ranking selection, and tournament selection), crossover operators, and mutation operators, which we will finish in the next lecture. Throughout this lecture, the basic tension between natural/artificial selection and drift is emphasized -- there is a downside to having selection operators that have high selection pressure (because they get stuck in suboptimal traps), but very low selection pressure can cause small populations to stagnate due to drift.

Whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/wbvlvpj1wkft5903pg1ad/IEE598-Lecture1E-2024-01-30-The_Basic_Genetic_Algorithm_and_Its_Implementation-Notes.pdf?rlkey=x81e1h1lv42vkwt8lphcr7uqr&dl=0

Lecture 1D (2025-01-28): The Four Forces of Evolution and The Drift Barrier

In this lecture, we review the four forces of evolution -- mutation, migration/gene flow, genetic drift, and natural selection -- and the contribution that each one makes to either increasing or decreasing variance in a population over time. So a more complete picture of evolution is a tense combination of these forces, each of them leading to different kinds of effects on the distribution of alleles (strategies) in a population. We discuss how the tendency for natural selection to produce higher quality solutions is ultimately limited by genetic drift that dominates when populations have low fitness diversity (low selective pressure), and we discuss how this sets up a speed–accuracy tradeoff between mutation (which counteracts drift in a way  that does not require more time for convergence but makes it impossible to fine tune solutions) and population size (which can fine tune solutions but requires a longer time to converge to a good solution). Selection operators and evolutionary hyper-parameters should be chosen with these pressures and tradeoffs in mind.

Whiteboard notes for this lecture are archived at:
https://www.dropbox.com/scl/fi/09u3vd7l3m9h68pc5uyr9/IEE598-Lecture1D-2024-01-28-The_Four_Forces_of_Evolution_and_The_Drift_Barrier-Notes.pdf?rlkey=u4id8rcd9ou45og4eqxx5fu38&dl=0

Lecture 1C (2025-01-21): Population Genetics of Evolutionary Algorithms

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/scl/fi/0v7cr220534e0fsivf803/IEE598-Lecture1C-2024-01-21-Population_Genetics_of_Evolutionary_Algorithms-Notes.pdf?rlkey=o87azad7gjivlbd1ylgzt8s26&dl=0

Lecture 1B (2025-01-16): Evolutionary Approach to Engineering Design Optimization

In this lecture, we formally introduce the Engineering Design Optimization (EDO) problem and several application spaces where it may apply. We then discuss classical approaches for using computational methods to solve this difficult optimization problem -- including both gradient-based and direct search methods. This allows us to introduce trajectory and local search methods (like tabu search and simulated annealing) and population-based methods (like the genetic algorithm, ant colony optimization, and particle swarm optimization). We then start down the path of exploring evolutionary algorithms, a special (but very large) set of population-based methods. In the next lecture, we will connect this discussion to population genetics and a basic Genetic Algorithm (GA).

The whiteboard notes taken during this lecture can be found at:
https://www.dropbox.com/scl/fi/ifkopelwknhhbj0wuz8ws/IEE598-Lecture1B-2024-01-16-Evolutionary_Approach_to_Engineering_Design_Optimization.pdf?rlkey=3xdr40xw5qjz62uqzrd373hh6&dl=0

Lecture 1A (2025-01-14): Introduction to the Course, Its Policies, and Its Motivation

In this first lecture of the semester, I introduce the course and its expectations and policies. After highlighting several sections of the syllabus, we pivot to discussing heuristics, metaheuristics, and hyperheuristics more broadly. This sets up for the next lecture which will introduce Evolutionary Algorithms, for use as metaheuristics.

The whiteboard notes for this lecture can be found at:
https://www.dropbox.com/scl/fi/7ojfu7w0d58yhsb2zfhjb/IEE598-Lecture1A-2025-01-14-Introduction_to_Course_Policiesi_and_Motivations-Notes.pdf?rlkey=uw6va7dybspd2cvqtq1bui5f6&dl=0