Artificial Intelligence II
LP II (second quarter), 2007
Basic information
Lecturer and examiner:
Mattias Wahde, tel. 772 3727, e-mail: mattias.wahde@chalmers.se
Course assistant:
Krister Wolff, tel. 772 3625, e-mail. krister.wolff@chalmers.se
Literature:
Wahde, M. An introduction to evolutionary computation
Wahde, M. An introduction to neural networks
Wahde, M. Particle swarm optimization
Wahde, M. Ant colony optimization
Problem collection: Evolutionary algorithms
All course literature is provided free of charge.
Preliminary program
|
Date |
Time |
Lecturer |
Room |
Contents |
|
20071113 |
13.00-15.45 |
MW |
SJ |
Course introduction, biological basis of evolutionary algorithms (EAs) |
|
20071116 |
13.00-15.45 |
MW |
SJ |
Basics of EAs |
| 20071120 | 13.00-15.45 | MW | LT | Using EAs, properties of EAs |
| 20071123 | 13.00-15.45 | KW | LT | Advanced topics |
| 20071130 | 13.00-15.45 | KW | SJ | Linear genetic programming (LGP), applications of EAs, |
| 20071204 | 13.00-15.45 | KW | SJ | Introduction to neural networks |
| 20071214 | 13.00-15.45 | MW | LT | Backpropagation assignment (handout) |
| 20071218 | 13.00-15.45 | MW | SJ | Particle swarm optimization |
| 20071220 | 13.00-15.45 | MW | SJ | Ant colony optimization |
| 20080116 | 09.00-13.00 | SJ | Exam |
SJ = Steve Jobs, LT = Linus Torvalds
Examination
The examination will consist of a set of home problems, and an exam at the end of the course.
Home problem: The problem sheet will be handed out on 20071214, and should be handed in no later than 20080107. Maximum score: 25p
For problem 2, you need the following data set: TSPcities2.m. The paths (for problem 2.1) can be plotted in many different ways. Here are three Matlab functions that you may use (you may, of course, write your own functions as well): InitTSPPlot.m, InitConnections.m, PlotPath.m. The PlotPath function requires a path in the form of a vector of city indices.
For problem 3, you may start from the file BP.m.
Exam: The exam will take place on 20080116, 09.00-13.00. Maximum score: 25p
The requirements for the various grades are as follows:
A minimum of 10p is required (Note!) on the exam. Grades will be set according to
ECTS:
| A | Total score in [44, 50] |
| B | Total score in [37, 43.5] |
| C | Total score in [32, 36.5] |
| D | Total score in [24, 31.5] |
| E | Total score in [20, 23.5] |
Chalmers
| 5 | Total score in [42, 50] |
| 4 | Total score in [32, 41.5] |
| 3 | Total score in [20,31.5] |
Additional course material:
Scientific papers:
Slides: (from lectures)
20071113: Course introduction, biological basis of evolutionary algorithms
20071116: Basics of EAs
20071120: Using EAs, properties of EAs
20071123: Advanced topics
20071130: Linear genetic programming, applications of GAs
20071204: Introduction to neural networks
20071214: Backpropagation
20071218: Particle swarm optimization
20071220: Ant colony optimization
Programs:
20071120: GA function maximizer
Frequently asked questions:
Last update: 20071220, 09.52
