Lecture 1B (2022-01-13): The 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/s/st88vx87y6tv0xo/IEE598-Lecture1B-2022-01-13-The_Evolutionary_Approach_to_Engineering_Design_Optimization.pdf?dl=0