3 credits
Fall 2026 Lecture Upper Division
This course is about algorithms that are inspired by naturally occurring phenomena and applying them to optimization, design and learning problems. The focus is on the process of abstracting algorithms from the observed phenomenon, their outcome analysis and comparison as well as their "science". This will be done primarily through the lens of evolutionary computation, swarm intelligence (ant colony and particle-based methods) and neural networks.
Learning Outcomes1Describe the natural phenomena that motivate the discussed algorithms.
2Understand the strengths, weaknesses and appropriateness of nature-inspired algorithms.
3Apply nature-inspired algorithms to optimization, design and learning problems.
4Understand fundamental concepts of NP-hardness and computational complexity.
5Prove algorithm convergence rates using probabilistic arguments.
6Perform appropriate analyses on and between the outputs of stochastic algorithms.
7Analyze search space structure using statistical and information theoretic measures and explain its impact on algorithm behavior and output.
Prerequisites
Restrictions
made by Purdue students.