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, 2026.
- Vehicle routing problem - deadline April 20, 2026.
- Neuroevolution - deadline May 17, 2026.
You will submit your solutions to the Postal Owl. You can enroll in this system here. Always submit a Jupyter notebook with all the code needed to generate the outputs (tables, plots). Use a similar format as in the materials for the tutorials, i.e. interleave code and explanations.
Topics covered
| Date | Topic |
|---|---|
| Feb 23 | Introduction - Python libraries for ML |
| Mar 2 | Reinforcement Learning |
| Mar 9 | Evolutionary Algorithms - introduction |
| Mar 16 | Evolutionary Algorithms - continuous optimization |
| Mar 23 | Evolutionary Algorithms - genetic programming |
| Mar 30 | Swarm Optimization - PSO, ACO |
| Apr 6 | Easter Monday (tutorials cancelled) |
| Apr 13 | Neural Networks - Introduction |
| Apr 20 | Neural Networks - RBF and recurrent networks |
| Apr 27 | Neural Networks - Convolutional Networks |
| May 4 | Neuroevolution |
| May 11 | Deep Reinforcement Learning |
| May 18 | Artificial Life |