Nature Inspired Computing

Learning Outcomes

• Demonstrate fundamental insights of nature-inspired computation
• Implement nature-inspired methods into concrete algorithms
• Apply nature-inspired algorithms to some search and optimization applications
• Read relevant scientific research papers.

Course contents

1. Introduction to optimization and algorithms, Introduction to Nature Inspired Computing
2. Evolutionary Algorithms
3. History of evolutionary computation Components of evolutionary algorithms Major algorithms: genetic algorithms, differential evolution, evolution strategies, evolution programming, genetic programming
4. Swarm Intelligence Algorithms
5. Introduction to swarm intelligence algorithms, Basic swarm intelligence algorithms, Applications of swarm intelligence algorithms, Criticism on swarm intelligence algorithms.
6. Physically inspired Algorithms
7. Simulated Annealing
8. Nature-Inspired Computing in Applications
9. Implementing Some Algorithms, Measuring algorithms' quality: performance and robustness, Handling constraints of a problem, Handling multiple objectives, Nature Inspired Computing in Machine learning (clustering and optimization)

References/Reading Materials

1. A. E. Eiben, J. E. Smith: Introduction to Evolutionary Computing, Springer, 2003
2. Nature-Inspired Meta-heuristic Algorithms, Xin-She Yang, Luniver Press, 2008.
3. Swarm Intelligence, James Kennedy, and Russell C. Eberhart, Morgan Kauffman, 2001.