Nature-Inspired Computing - BLG 525E

Lecturer	Assoc. Prof. Dr. A. Şima (Etaner) Uyar
Schedule	Tuesdays, 09:30-12:30
Classroom	5306

Announcements

Hyper-heuristics talk slides have been added to the class files section on the Ninova system.
Attendance to at least 70% of the lectures, excluding the midterm exam, is required for this course.
You need to fullfil a set of requirements to be able to enter the final exam. The requirements form will be posted here. Please check back.
You will be required to do a program implementation for your homeworks. So you should be able to program efficiently in C, C++, Java or Python.

How to choose a statistical test.
Sample exams from previous offerings of "Nature Inspired Computing" and "Evolutionary Computing" courses. Please note that these exams should in no way be the sole basis for your preparation for your own exams. They are just sample questions and exercises for you to work on. They are not an indication of how your own exams will be.

Tentative Schedule

Week	Date	Topic
1	07.02	* Introduction to course * Introduction to search and optimization * Local search techniques
2	14.02	* Local search techniques
3	21.02	* Local search techniques
4	28.02	* Evolutionary algorithms
5	06.03	* Evolutionary algorithms
6	13.03	* Evolutionary algorithms
7	20.03	* Introduction to swarm intelligence * Ant colony optimization
8	27.03	* Particle swarm optimization * Artificial immune systems
9	03.04	Midterm exam
10	10.04	* Working with nature inspired heuristics
11	17.04	* Other nature inspired heuristics
12	24.04	* Other nature inspired heuristics
13	01.05	* Constraint handling * Hybridization
14	08.05	* Advanced topics

Slides (May be changed. Changed files will be marked with NEW!)

Introduction to Course Topics	slides
Introduction to Search, Optimization and Decision Problems	slides on SLS web page
Stochastic Local Search Techniques	slides on SLS web page
Introduction to Evolutionary Computation	handouts
Genetic Algorithms	handouts - TSP
GA Summary	handouts
Example Problems	handouts
Genetic Programming	handouts
Evolutionary Strategies	handouts
Evolutionary Programming	handouts
Differential Evolution	handouts
Grammatical Evolution	handouts
Swarm Intelligence	handouts
Ant Colony Optimization	handouts
Particle Swarm Optimization	handouts
Artificial Immune Systems	handouts - tutorial
Working with Nature-Inspired Heuristics	handouts
Handling Constraints	handouts
Hybridization	handouts
Handling Multiple Objectives	handouts
Dynamic Environments	handouts

Stochastic Local Search, Foundations and Applications, H. H. Hoos, T. Stuetzle, Elsevier, 2005.
Metaheuristics: From Design to Implementation, El-Ghazali Talbi, Wiley, 2009.
Introduction to Evolutionary Computing, A. E. Eiben, J. E. Smith, Springer, 2003.
Ant Colony Optimization, M. Dorigo, T. Stuetzle, MIT Press, 2004.
Swarm Intelligence, J. Kennedy, R. Eberhart, Academic Press, 2001.
"Artificial Immune Systems Tutorial", U. Aickelin, D. Dasgupta, Chapter 13 of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Methodologies, Eds. E. Burke, G. Kendall, Springer, 2005.