Welcome to CSCI 340 — Fall 2024

Evolutionary computation is a family of powerful optimization algorithms often used to find solutions to computationally intractable problems. The study of these algorithms and their application to problems is a large research area within computer science. Course topics include combinatorial optimization, genetic algorithms, particle swarm optimization, search space analysis, multi-objective optimization, and neuroevolution. Research practices and technical writing will be emphasized for course assignments/projects.

The content used to generate the course can be found on GitHub

Professors

Dr. James Hughes

• jhughes at stfx.ca

• Annex 20B

Lecture

• Tue 8:30 - 9:25 (K1), MULH 4024

• Wed 10:30 - 11:25 (K2), MULH 4024

• Fri 9:30 - 10:25 (K3), MULH 4024

Office Hours

• Tue, 12:30 – 13:30

• Wed, 12:30 – 13:30

• Thr, 13:30 – 14:30

• Fri, 13:30 – 14:00

YouTube

YouTube

Course Notes

• 1. Introduction
• 2. Implementing a Genetic Algorithm
• 3. Experimenting with a Genetic Algorithm
• 4. Problems
• 5. Origin
• 6. Overview
• 7. Representation
• 8. \(n\) Queens Genetic Algorithm
• 9. Experimenting with the \(n\) Queens Genetic Algorithm
• 10. Genetic Operators
• 11. Selection and Population Management
• 12. Genetic Programming
• 13. Symbolic Regression with Genetic Programming
• 14. Experimenting with Symbolic Regression
• 15. Tuning Hyperparameters
• 16. What Comparing Distributions Means
• 17. Multi-Objective Problems
• 18. Particle Swarm Optimization
• 19. Implementing PSO

Student Lectures

• 1. Student Lecture Task Description
• 2. Ant Colony Optimization (ACO)
• 3. Evolutionary Art
• 4. Neuroevolution

Student Projects

• 1. Project Description
• 2. Snake

Assignments

• 1. Travelling Salesperson Problem
• 2. Genetic Programming
• 3. Particle Swarm Optimization

Outline

• Computer Science 340 — Evolutionary Computation