I. Course A. Catalog Description

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C
OLLEGE OF
N
ATURAL
S
CIENCES
&

M
ATHEMATICS


HTTP
://
NSM
.
UH
.
EDU



COURSE TITLE/SECTION
:
COSC 6367: Evolution
ary Programming


TIME:


TU/TH 11:30
-
1p






FACULTY:

Christoph F. Eick


OFFICE HOURS
:

TU 2
-
3:20p TH 1
-
1:40p





E
-
mail:

ceick@uh.edu


Phone:


713
-
743
-
3345


FAX:

713
-
743
-
3335



I.

Course


A.

Catalog Description

Evolutionary Programming Cr. 3 (3
-
0). Prerequi
sites: graduate standing, programming
experience, MATH 3336 or consent with instructor. Theory and application of evolutionary
programming and other related areas in evolutionary and natural computation centering on
genetic algorithms and programming, evol
ution st
r
ategies, artificial life, and other models
that rely on evolutionary principles. Students will perform course projects that apply the
discussed techniques to numerical optimization problems, to machine learning, and to the
simulation of biological

and cultural systems.


Course Website:
http://www2.cs.uh.edu/~ceick/6367/6367.html



II.

Course Objectives


Upon completion of this course, students

1.

have a sound background in the major evolutio
nary computing approaches genetic
algorithms, evolution strategy, genetic programming and classifier systems.

2.

will be knowledgeable in the theory of evolutionary computing systems

3.

will have practical experience in applying evolutionary computing to challen
ging
optimization problems, to machine learning, and
/or

to artificial life
, art, and design
problems.

4.

will obtain some hands
-
on experience in simulating, modeling, and understanding the
evolution of complex systems.

5.

will obtain some sound
background

in are
as that are closely related to evolutionary
:

namely
,

search techniques, numerical optimization, and
machine

learning.

6.

will have some background with respect to software libraries that are commonly
when
developing

evolutionary computing systems
.

7.

will have
some basic knowledge
about
artificial life
.

8.

will get some exposure to probabilistic algorithms in genera
l.

9.

will
obtain practical experience in conducting

medium sized research projects, and
in



present
ing

their findings in
reports and oral presentations.




I
II.

Course Content


20
1
2

This course will include the following topical (content) areas:

1.

Introduction to Evolutionary Computing

2.

Genetic Algorithms

3.

Evolution Strategy

4.

Brief Introduction to Numerical Optimization Problems

5.

Application of Evolutionary Computi
ng to
Optimization Problems

(Course Project1)

6.

Introduction to Search Techniques

7.

Genetic Programming

8.

Simulating, Modeling, and Understanding the Evolution of Complex Systems (potential
Project2)

9.

Classifier Systems

10.

Brief Introduction to Machine Learning, cen
tering on Adaptive Learning

11.

Using Genetic Programming and Classifier Systems to Design Adaptive Systems
(Course Project3)

12.


Parameter Adaptation and Control in Evolutionary Computing Systems

13.


Memetic Algorithms

14.


Brief
Introduction to Artificial Life

15.

Th
eory of Evolutionary Computing


IV.

Course Structure

The course will be very project oriented;
students
will develop medium
-
sized software
systems that employ evolutionary computing paradigms in 3 course projects. Alternatively,
students can choose

a subj
ect and give
s

a oral presentation (+ report or webpage) as your
third project

which is usually a group project
. In addition to regular lectures there will be a lot
of project related discussions during the lectures.

V.

Textbook

A.E. Eiben and J.E. Smith:
Introduction to Evolutionary Computing, Springer Verlag, 2003.

VI

Course Requirements


A.

Reading Assignments

Student obviously will read the textbook, and other written material

B.

Written Assignments

Students have to write reports that summarize their fi
ndings of the three course projects
.

C.

Projects

1. Application of EC to Solve
Challenging Optimization Problems

2. Students can choose between different projects and give a presentation

3
. Using EC to Develop Adaptive Systems


D.

Exams (as needed)

There
will be
2 quizzes and a final exam.




VII.

Evaluation and Grading


Exams (4
7
%), Course Projects (
51
%), Class Participation
/Extra Credit

(
2
%)

Policy on grades of I (Incomplete):
will only be given to students that completed more
than 50% of the course.


VIII
.

Consultation


IX.

Bibliography


Required Textbook



A.E. Eiben and J.E. Smith:
Introduction to Evolutionary Computing, Springer
Verlag, 2003.

Recommended
Textbooks
:




Kenneth A. De Jong
Evolutionary Computation
---

A Unified Approach, MIT Press,
2006



Th. Bä
ck:
Evolutionary Algorithms in Theory and Practice: Genetic Algorithms,
Evolution Strategies,


Evolutionary Programming
, Oxford University Press, New
York

1996.



Z. Michalewicz:
Genetic Algorithms + Data Structures = Evolution Programs
.
Springer Verlag, Be
rlin, 1996.

Further
R
eading:




Th. Bäck, D.B. Fogel, Z. Michalewicz:
Handbook of Evolutionary Computation
,
Institute of Physics

Publishing, Bristol UK and Oxford University Press, Philadelphia
PA, 1997.
(New, cheaper edition
Vols. 1 and 2, Institute of Phy
sics Publishing,
Bristol UK
, 2000).



W. Banzhaf, P. Nordin, R.E. Keller, F.D. Francone:
Genetic Programming
, Morgan
Kaufman

Publis
hers, San Francisco, CA, 1998.



L. Davis:
Handbook of Genetic Algorithms
, Van Nostrand Reinhold, 1991.



D.B. Fogel:
Evolutiona
ry Computation. Toward a new Philosophy of Machine
Intelligence
, IEEE Press,
Piscataway, NJ
, 1995.



D.E. Goldberg: Genetic Algorithms in Search, Optimization, and Machine L
earning.
Addison
-
Wesley,

1989.



J. Koza: Genetic Programming I, MIT Press, Cambridge

MA
, 1992.



J. Koza: Genetic Programming II, MIT Press, Cambridge MA
, 1994.



J. Koza: Genetic Programming III, MIT Press, Cambridge MA
, 1998.



M. Mitchell: An Introduction to Genetic Algorithms. MIT Press, Cambridge MA,
1996.


The university requires all
syllabi to have the following paragraph:

Addendum:
Whenever possible, and in accordance with 504/ADA guidelines, the
University of Houston will attempt to provide reasonable academic accommodations to



students who request and require them. Please call 713
-
743
-
5400 for more assistance