Lecture 2A: GA Approaches Beyond Optimization - Artificial Immune Systems (2020-01-29)

This lecture closes out our discussion of the basic Genetic Algorithm (GA) and introduces closely related approaches for problems not directly related to optimization. In this lecture, we cover Artificial Immune Systems (AIS), a topic from immunocomputing. We cover the basic clonal selection and negative selection/elimination methods for artificial immune systems (as well as some very basic biology about the immune system, both innate and adaptive/specific).

Notes taken during this lecture can be found at:
https://www.dropbox.com/s/8rexbjaefvslq7c/IEE598-Lecture2A-Notes.pdf?dl=0

Lecture 1D: Implementing the Genetic Algorithm, Part 2 (2020-01-27)

Continued coverage of details of how the Genetic Algorithm is implemented, with a focus on the selection and mutation operators. A PDF of the lecture notes can be found at: https://www.dropbox.com/s/61u8vnlsfd4ed7c/iee598-lecture1d-notes.pdf?dl=0

Lecture 1C: Implementing the Genetic Algorithm, Part 1 (2020-01-22)

Introduction to details of how the Genetic Algorithm is implemented, including an introduction to the choice of hyperparameters and genetic operators that can balance selection pressure to maximize the effectiveness of the GA. A PDF of the lecture notes is linked from this blog post.

Lecture 1B: Basics of Evolutionary Algorithms and Population Genetics (2020-01-15)

The prototypical structure of evolutionary algorithms are presented, along with the background context of direct search metaheuristics (including tabu search). Preparing for the introduction of the genetic algorithm in the next lecture, basic biological terms from population genetics are covered, culminating in a brief discussion of genetic drift and how it applies to evolutionary algorithms for optimization.

Lecture 1A: Introduction to Course, Metaheuristics, and Evolutionary Algorithms (2020-01-13)

Today's lecture introduces the course, its structures, and its policies. Then a basic introduction to metaheuristics is given, leading up to a few introductory words about evolutionary algorithms. A PDF of the digital lecture notes has been linked from this blog post.