This class covers stochastic methods of optimization, primarily simulated annealing, evolutionary strategies, and genetic algorithms. The class is 10 hours total and uses HTML presentations and Jupyter notebooks in Python for exercises. The evaluation for this class will be based on presenting an article on a stochastic algorithm in teams of 3.
| Schedule | ||
|---|---|---|
| 04/11 | Introduction and simulated annealing | Continuous optimization, random search, simulated annealing |
| 10/11 | Evolutionary Strategies | Population-based methods, 1+1 ES, CMA-ES |
| 17/11 | Genetic Algorithms | Genetic Algorithm, Multi-Objective Optimization, NSGA-II |
| 25/11 | Project | Project presentations |