IEE 598: Lecture 1B (2024-01-16): 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