The course gives a comprehensive overview of the field through a series of lectures and exercises. In addition, a practical application exercise of natural computing algorithms is given to the students, who are expected to run experiments and write a short report about the experiment and the results obtained.
Natural computing is a quickly developing field dealing with models and computational paradigms inspired by nature and attempts to understand the world around us in terms of information processing.
Natural computing today includes paradigms such as modelling information processing through artificial neural networks, modelling emergent behaviour resulting from the interaction of a large collection of agents in particle swarms (representing e.g., birds, insects) or spatial arrangements of cells (cellular automata), or modelling efficient search and optimization procedures such as ant colonies (finding shortest paths in a network of possibilities), simulated annealing processes (finding the optimal energy state of a crystal), and evolutionary processes (adapting a population to find the best mix of genetic material under changing environmental conditions).
The course introduces the foundations of a variety of such computational paradigms, and discusses algorithmic implementations on computers as well as the analogies between these implementations and the natural model. In addition, we also present some practical application examples of such computational paradigms, such as pattern recognition, engineering optimization, simulations of fire breakouts, to name a few.
No special prerequisites required.
The following book is recommended but not mandatory for the course:
Leandro Nunes de Castro – Fundamentals of Natural Computing
Chapman & Hall/CRC.
Table of Contents:
For all paradigms, we will discuss the natural model, the algorithm(s) resulting as an abstraction of the natural model, and the relationship between the two (i.e., how close is an algorithm to its natural paradigm?).
a. What is Natural Computing
b. Common terms and definitions
- Simulated Annealing
- Swarm Intelligence
- Particle Swarm Optimization
- Ant Colony Optimization
- Cellular Automata
- Neural Networks
- Fractal Geometry
- Firefly Systems
- Artificial Immune Systems
- DNA Computing
- Evolutionary Algorithms
- Other Paradigms
Slides will be provided to the students for download.
The final grade is a combination of grades for
(1) the written exam (70%) and
(2) the report about the practical assignment (30%).