Nature Inspired Algorithms
The aim of the lecture is to provide an introduction to nature-inspired algorithms, such as evolutionary algorithms, neural networks, etc.
The materials for the lecture and tutorials are published on this page.
The exam will be oral with time for preparation. The exam should test the topics covered in the lecture and their applications. A detailed description of the exam and a list of examined topics is on a separate page.
Lesson Plan
Tutorials
In the tutorials, we will implement some of the algorithms/models from the lecture and will experiment with them. In order to obtain the credit, you need to solve three homework assignments that will be published during the semester.
Assignments
- Knapsack problem - deadline March 30, 2025.
- Vehicle routing problem - deadline April 20, 2025.
- Neuroevolution - deadline May 25, 2025.
Topics covered
| Date | Topic |
|---|---|
| Feb 18 | Introduction - Python libraries for ML |
| Feb 25 | Reinforcement Learning |
| Mar 4 | Evolutionary Algorithms - introduction |
| Mar 11 | Evolutionary Algorithms - continuous optimization |
| Mar 18 | Evolutionary Algorithms - genetic programming |
| Mar 25 | Swarm Optimization - PSO, ACO |
| Apr 1 | Neural Networks - Introduction |
| Apr 8 | Neural Networks - RBF and recurrent networks |
| Apr 15 | Neural Networks - Convolutional Networks |
| Apr 22 | Tutorial cancelled to keep synchronization with lecture |
| Apr 29 | Neuroevolution |
| May 6 | Deep Reinforcement Learning |
| May 13 | Artificial Life |
| May 20 | ??? (Probably cancelled) |