Syllabus
- Biological inspiration in the design of algorithms and models a. evolutionary models b. neural models
- Evolutionary algorithms a. Simple genetic algorithm b. Representation, genetic operators, fitness, selection c. Evolutionary algorithms for continuous optimization d. Neuro-evolution, algorithm NEAT e. Genetic programming
- Swarm algorithms a. Ant Colony Optimization b. Particle Swarm Optimization
- Neural networks a. Perceptron, multi-layered perceptron, back-propagation as a learning algorithm b. Convolutional networks c. RBF networks a Kohonen’s maps
- Other nature inspired algorithms a. Artificial Immune Systems b. Cellular Automata c. Artificial Life
- Applications in optimization and machine learning a. Continuous and combinatorial optimization b. Multi-objective optimization c. Supervised and unsupervised learning, reinforcement learning
Annotation
The goal of the lecture is to introduce the main nature-inspired algorithms (evolutionary algorithm, neural networks, …) and how they can be applied to solve problems in optimization and machine learning. In the seminar, some of the algorithms will be implemented and used to solve simple problems in the areas mentioned above.