COMPUTAZIONE NATURALE

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

Classroom-taught lessons. Can be also included external seminars held by expert researchers on related topics.

Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.

Learning assessment may also be carried out on line, should the conditions require it.

• What is, and how works the Natural Computation
• Computability theory: an introduction to NP-Complete problems
• Basic Concepts
• Introduction to basic concepts on Machine Learning and Computational Learning Theory
• Graph Theory: an introduction to the basic concepts; properties; and theorems
• Landscape, Search Space, and Optimization Models
• No Free Lunch Theorem
• Bio-Inspired Algorithms: Genetic Algorithms; Genetic Programming; Immune-Inspired Computation; Swarm Intelligence and Differential Evolution
• Bio-Inspired Algorithms for Multi-Objective Optimization and Decision Making
• Hybrid Bio-Inspired Algorithms
• Parallel and Distributed Bio-Inspired Algorithms
• Examples of bio-inspired algorithms application in: Network Sciences; Games; Internet of Things; Computer Security; Robotics; Art and Design.

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.