Welcome to

Neural and Evolutionary Computing IV 1997/98

Neural and evolutionary computing is an emerging technology in advanced computer-applications. It is about using computers to learn, optimise and discover new engineering solutions and technologies by mimicking human intelligence in the brain and genetic systems. It has been proven very useful in learning and automating (as well as discovering new) designs for electronic/electrical systems, circuits, filters, motors, drives and control systems. This course however requires little mathematics or programming.

I. Laboratory and Distance-Learning/Experimentation

Experiment 1: What Evolutionary Computing is/How a Genetic Algorithm Works?

Press here for an interactive answer and hands-on excercise.

Experiment 2: Solving the Travelling Salesman's Problem by Learning

Experiment 3: MATLAB Electronic Tutorials on WWW


With access to both the WWW and to Matlab on either a personal computer or a workstation, any student will be able to follow a tutorial while running Matlab and will be able to easily switch between the two programs. Students will be able to grasp key concepts and design techniques in a "learn by seeing and doing" manner.

Commands shown in the tutorials can be copied from the Netscape, MSIE(?) or Mosaic window and pasted into the Matlab command window or into an m-file with a simple point and click of the mouse; there is no need for time-consuming typing and editing. Students can immediately see the result of an actual computation, compare it to the result shown on the tutorial, and quickly experiment with modifications of the commands and changes of parameters.

Experiments 4: FlexTool(GA) MATLAB Toolbox and Evolutionary Optimisation

Follow the instructions given in the Laboratory Sheets.

Experiment 5: Neural Network Toolbox and Neural Learning

Follow the instructions given in the Laboratory Sheets.

Backpropagation Learning

Application in Noise Cancellation

Hebbian Learning

Solving Neural Learning Problems in Tutorial 2

Experiment 6: Neural Learning and Fuzzy Systems - IEE/IEEE Video Tape and CD-ROMs
(Optional and during spare time only)

The IEE Video Tape shown at the lecture can be viewed again from the CD-ROM: "IEE Distance Learning - Neural Computing Video Course (SO2). You can run this on the Pentium 133 PCs, which have a sound card. Ask Mr. O'Hara, the Technician, for a pair of speakers. You can also use the CD to see the demonstration on how neural networks work. Note that other options do not work on the CD!

You can also borrow the IEEE/ABAS "Learning Neural Networks" CD-ROM from Mr. O'Hara.

"1993 IEEE International Conference on Neural Networks" CD-ROM also available.

"Second IEEE International Conference on Fuzzy Systems" CD-ROM also available.


II. Further Information

This page must not be used alone and must be used in conjunction with NEC4 Lecture Notes, Appendices, Tutorial Sheets, Laboratory Sheets and Assignment Sheets. The IEE and IEEE video tape and CD-ROMs used in the course may be borrowed from the Lecturer, Dr. Y. Li.

Syllabus is here. Sister Course to merge later: Image Processing and Pattern Recognition IV by Prof John Barker.


III. Recommended Books

Authors Title, edition Publisher Year ISBN Cost  Code
Z Michalewicz Genetic Algorithms + Data Structures = Evolution Programs Springer-Verlag, 2nd Ed 1994 3540580905 £25 B
S V Kartalopoulos Understanding Neural Networks and Fuzzy Logic IEEE Press 1996 0780311280 $35 B
David E Goldberg Genetic Algorithms in Search, Optimisation and Machine Learning Addison-Wesley 1989 0201157675 £24 C

Codes : A = compulsory; B = strongly recommended; C = recommended; D = wider reading


IV. Tutorials

Tutorial 1

Tutorial 2


V. Mini-Project Assignment

Completed assignment must be submitted to Dr. Li (into a "run-about" mailbox just outside B1-16) by the end of Week 12, Friday, 23 January 1998. If you have genuine difficulties beyond your control, however, you may be allowed to submit the entire mini-project by Week 13, but you must contact Dr. Li in the first instance to submit an outline plan of your mini-project by Week 11. Submitting a floppy containing your programs is voluntary, but you may be asked to do so for random inspection.

If your report is of value for future teaching, we would like to ask for your contribution to this course the next year.

Which algorithm might be the most appropriate to your application at hand:
NB. Swarm intelligence such as particle swarm optimisation and ant colony optimisation fits in the 'evolutionary programming' - 'evolution strategies' branch for continuous or numerical optimisation and fits in the 'genetic algorithm' branch for discrete or structural optimisation. 


The following classification illustrates the complexity of computational problems, where NP-complete are a class of computational problems that cannot be solved in deterministic polynomial (P) time but can be solved in nondeterministic polynomial (NP) time.  Given the modern computer simulation power, many virtual engineering problems can now be solved via digital prototyping, although in exponential time (i.e., they may be theoretically solvable but practically intractable).  The power of evolutionary computation lies in its ability to solve many exponential problems in NP time, i.e., to make exponential problems practically solvable.  Some problems such as winning the lottery, however, remain an exponential problem (i.e., is 'exponential-complete' and can only be solved by enumerations). 


VI. NEC4 Related Newsgroups


VII. Evolutionary Computing Research at Glasgow


All materials provided by this site are copyright protected and are for use with Glasgow University's Neural and Evolutionary Computing IV (1995-97) course only. It is illegal to copy, use or distribute any such material for any other purposes without the consent of the course Lecturer, Dr. Y. Li.