BIO-INSPIRED AND NATURAL COMPUTATION

The aims of the course are focused on the design and develop of algorithms that take inspiration from nature and biology, as well as the key features required for developing a successful algorithm. All bio-, or nature-inspired algorithms will be analysed and applied in the following application fields: optimization; anomaly detection; decision theory; and game theory.

The goal of the course is to provide to each student:
1) good knowledge on the basic concepts;
2) good knowledge on the "intelligent systems" and their designing;
3) excellent ability in developing an efficient bio-, or nature-inspired algorithm;
4) problem-solving

• What is, and how works the Natural Computation
• Computability theory: an introduction to NP-Complete problems
• Graph Theory: an introduction to the basic concepts; properties; and theorems
• Complex Systems: a short introduction
• Modeling Complex Systems
• Landscape e Search Space
• Optimization Models
• Metaheuristics: a principle concepts
• Single-solution based Metaheuristics
• Population based Metaheuristics

1. E.G. Talbi, "Metaheuristics: From Design to Implementation", Wiley, 2009
2. C. Blum and G.R. Raidl, "Hybrid Metaheuristics: Powerful Tools for Optimization", Artificial Intelligence: Foundations, Theory, and Algorithms, 2016
3. Slides.