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                 C A L L  F O R  P A R T I C I P A T I O N S

         AI'94 Tutorial: An Introduction to Evolutionary Computation

                                    by

			    Prof Z. Michalewicz 
			Department of Computer Science 
		  University of North Carolina --- Charlotte 
			Charlotte, NC 28223, U.S.A. 
		      Email: zbyszek@mosaic.uncc.edu 
			   Phone: (704)547-4873 
			    Fax: (704)547-3516 
	
				   and

				Dr X. Yao  
			Department of Computer Science  
	   University College, The University of New South Wales  
	Australian Defence Force Academy, Canberra, ACT 2600, Australia  
		      Email: xin@csadfa.cs.adfa.oz.au 
			   Phone: +61 6 268 8819 
			    Fax: +61 6 268 8581

			     21 November 1994


INTRODUCTION

Evolutionary computation is the study of computational systems which use ideas
and get inspirations from natural evolution. This full-day tutorial will give 
a practical introduction to genetic algorithms, evolutionary programming and 
evolution strategies and present a brief overview of the whole field of
evolutionary computation.

OUTLINE OF THE TUTORIAL

This tutorial will cover both evolutionary optimisation and evolutionary 
learning. It consists of three sessions. One of them is allocated for 
practical experiments in which the participants will have an opportunity to go 
through some examples and get a better idea of how evolutionary optimisation 
works in practice. The tutorial is mainly designed for newcomers to the field 
of evolutionary computation. The topics covered by the tutorial include the 
following:

Session~1
---------
    1. Introduction 
	(a) A Definition of Evolutionary Computation
	(b) Major Branches of Evolutionary Computation
    2. Genetic Algorithms 
	(a) A Simple Genetic Algorithm
	(b) Selection
	(c) Crossover
	(d) Mutation
	(e) Other Genetic Operators

Session~2
---------
    1. Evolution Strategies and Evolutionary Programming
	(a) A Brief History
	(b) Adaptive Mutation
    2. Hybrid Algorithms
    3. Evolutionary Learning
	(a) Classifier Systems
	(b) Evolutionary Artificial Neural Networks
	(c) Artificial Life

Session~3
---------
    Practical experiments.

BIO-DATA OF THE PRESENTERS

Dr Zbigniew Michalewicz is a professor at the Department of Computer Science,
University of North Carolina---Charlotte, Charlotte, NC 28223, USA. He is
one of the leading researchers in the field of evolutionary computation. 
He has published a monograph and numerous technical papers in international
journals and conference proceedings. He is the general chairman of the First 
IEEE International Conference on Evolutionary Computation held in Orlando, 
27--29 June 1994. He is also in the program committee of many other
international conferences. Professor Michalewicz is a member of the editorial
board of the journal "Evolutionary Computation" and "Statistics and 
Computing". He is a member of the ACM.

Dr Xin Yao is a lecturer at the Department of Computer Science, University 
College, the University of New South Wales, Australian Defence Force Academy 
at Canberra. His research interests include evolutionary artificial neural 
networks, evolutionary optimisation, simulated annealing, and evolutionary and 
hybrid learning systems. He has published more than 20 technical papers 
in international journals and conference proceedings. He co-organised AI'93 and
AI'94 Workshops on Evolutionary Computation and is in the Program Committee of 
ICYCS'93, IEEE EC'95, IEA-AIE'95. He is a member of IEEE and IEEE Computer 
Society.

PREREQUISITES

1. Rudimentary knowledge of probability.
2. Rudimentary knowledge of UNIX (required only by the practical session).

-------------------------------------------------------------------------------
       Seventh Australian Joint Conference on Artificial Intelligence (AI'94) 

                       STRUCTURED SEQUENCE TUTORIALS

A structured sequence of pre-conference tutorials on several aspects of
applied AI has been organised. Tutorial participants will be able to select
from a choice of tutorials to suit their specialist requirements.

The following are the list of tutorials organised for AI'94. All
participants of any of these tutorials may attend the talk entitled
"Introduction to Artificial Intelligence". This is a complementary session
for all tutorial participants.
                    

Guide:
(y) - indicate YES
(n) - indicate NO
(t) - indicate THEORETICAL SESSION
(p) - indicate PRACTICAL SESSION
(d) - indicate DEMONSTRATION SESSION
1hr - indicate one hour
4s  - indicate four sessions
3s  - indicate three sessions 

Note: Each session is One and a Half hours long.


--------------------------------------------------------------------------------
No.     Tutorial Title               Presenters            CODE Length Practical
--------------------------------------------------------------------------------
[0] Introduction To Artificial       Not Confirmed yet     AI     1hr      -
    Intelligence

[1] Constraint-Based Reasoning       Dr. H. W. Guesgen     CBR    4s       y

[2] Fundamentals of Fuzzy Logic      Dr. A. Sekercioglu    FLFC   3s       y
    and Fuzzy Logic Controllers      G.K. Egan
               
[3] An Introduction to Evolutionary  Dr. X. Yao            EC     3s       y
    Computation                      Prof Z. Michalewicz
               
[4] Building Intelligent Decision    Dr. J. Zeleznikow     IDSS   3s       y
    Support Systems through the use  Mr. Dan Hunter
    of multiple reasonig Strategies
                
[5] Intelligent Learning Database    Dr Xindong Wu         ILDB   3s       y 
    Systems            

[6] Nonmonotonic Reasoning           Dr. M-A Williams      NMR    3s       y
                                     Dr. G. Antoniou

[7] Theoretical Foundations of       Dr. A. G. Hoffman     TFML   3s       n
    Machine Learning                 Dr. Shyam Kapur
                                     Dr. Arun Sharma

[8] Knowledge Acquisition and        Dr. P. Compton        KAM    3s       d
    Maintenance with Ripple Down 
    Rules     

[9] Hybrid (AI symbolic,             Dr. Nik K. Kasabov    HS     3s       d
    Connectionist, Fuzzy, Chaotic) 
    Systems


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Tentative Tutorial Timetable
--------------------------------------------------------------------------------

Monday 21/11/94 (Day 1)
=======================
 9.30 - 10.30:  Introduction to AI
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  NMR(t)  IDSS(t) EC(t)   TFML(t)
 3.30 -  4.00:  Afternoon Tea Break     
 4.00 -  5.30:  NMR(p)  IDDS(p) EC(p)   TFML(t)

Tuesday 22/11/94 (Day 2)
========================
 9.00 - 10.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
10.30 - 11.00:  Morning Tea Break
11.00 - 12.30:  CBR(t)  ILDB(t) FLFC(t) KAM(t)  HS(t)
12.30 -  2.00:  Lunch
 2.00 -  3.30:  CBR(t)  ILDB(p) FLFC(p) KAM(d)  HS(d)   
 3.30 -  4.00:  Afternoon Tea Break
 4.00 -  5.30:  CBR(p)                               

--------------------------------------------------------------------------------
Examples of structured sequence of tutorials:
--------------------------------------------------------------------------------

There are couple of structured sequence of tutorials that one could adopt.
For example, if the participant is interested in logic/theoretical basis of
AI, then he/she may want to select the following sequence:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: NMR(t)
        2.00 -  3.30: NMR(t)
        4.00 -  5.30: NMR(p)

Day 2   9.00 - 10.30: CBR(t)    
       11.00 - 12.30: CBR(t)
        2.00 -  3.30: CBR(t) 
        4.00 -  5.30: CBR(p)

Alternatively, if the participant is more interested in the applications of
AI, then the following sequence may be more suitable:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: IDSS(t)
        2.00 -  3.30: IDSS(t)
        4.00 -  5.30: IDSS(p)

Day 2   9.00 - 10.30: FLFC(t)
       11.00 - 12.30: FLFC(t)
        2.00 -  3.30: FLFC(p) 


If the interest is in Machine Learning/Knowledge Acquisition, then the
possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI 
       11.00 - 12.30: TFML(t)
        2.00 -  3.30: TFML(t)
        4.00 -  5.30: TFML(t)

Day 2   9.00 - 10.30: ILDB(t)  or  KAM(t)    
       11.00 - 12.30: ILDB(t)  or  KAM(t)
        2.00 -  3.30: ILDB(p)  or  KAM(d)

Another possible sequence may be:

Day 1   9.30 - 10.30: Introdution to AI
       11.00 - 12.30: EC(t)
        2.00 -  3.30: EC(t)
        4.00 -  5.30: EC(p)

Day 2   9.00 - 10.30: HS(t)    
       11.00 - 12.30: HS(t)
        2.00 -  3.30: HS(d)
--------------------------------------------------------------------------------
Further Information:
--------------------------------------------------------------------------------
For further information on the structured sequence tutorials, please
contact the AI'94 Tutorial Co-ordinator, at the following address:

                        Dr. Dickson Lukose
         Department of Mathematics, Statistics, and Computing Science
                    University of New England
                        Armidale, N.S.W., 2351
                             AUSTRALIA

                 e-mail: ai94@fermat.une.edu.au
                 fax.  : (+61 67) 73 3312
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