Department of Electrical and
Computer Engineering
EEC
693/793, ESC 794
Special
Topics: PopulationBased Optimization (4 credit hours)
Fall
2008
Description: This course discusses the theory, history, mathematics, and applications of populationbased optimization algorithms, most of which are based on biological processes. Some of the algorithms that are covered include genetic algorithms, evolutionary computing, ant colony optimization, biogeographybased optimization, differentical evolution, and artificial immune systems. Students will write computerbased simulations of optimization algorithms using Matlab. After taking this course the student will be able to apply populationbased algorithms using Matlab (or some other high level programming language) to realistic engineering problems. This course will make the student aware of the current stateoftheart in the field, and will prepare the student to conduct independent research in the field.
Text:
J. Kennedy, R. Eberhart, and Y. Shi, Swarm
Intelligence, Morgan Kaufmann Publishers, 2001
References: M. Batty,
Cities and Complexity, MIT Press,
2005
D. Coley, An Introduction to
Genetic Algorithms for Scientists and Engineers, World Scientific,
1999
L. Davis, Handbook of Genetic
Algorithms, Van Nostrand Reinhold, 1991
L. de Castro, Fundamentals of Natural Computing, CRC
Press, 2005
A. Engelbrecht, Computational Intelligence, John Wiley
& Sons, 2007
D. Fogel, Evolutionary Computation: The Fossil
Record, IEEE Press, 1998
N. Forbes, Imitation of Life, MIT Press, 2005
M.
Gen and R. Cheng, Genetic Algorithms and
Engineering Design, John Wiley & Sons, 1997
M. Gen and R. Cheng, Genetic Algorithms and Engineering
Optimization, John Wiley & Sons, 2000
D. Goldberg, Genetic Algorithms in
Search, Optimization, and Machine Learning, AddisonWesley,
1989
T. Gonzalez, Handbook of
Approximation Algorithms and Metaheuristics, CRC Press, 2007
R. Haupt and
S. Haupt, Practical Genetic
Algorithms, John Wiley & Sons, 1998
J. Holland, Adaptation in Natural and Artificial
Systems, MIT Press, 1992
M. Jamshidi, Robust Control Systems with Genetic
Algorithms, CRC Press, 2003
J. Koza, Genetic Programming, MIT Press,
1992
Z. Michalewicz, Genetic
Algorithms + Data Structures = Evolution Programs, Springer, 1996
M.
Mitchell, An Introduction to Genetic
Algorithms, MIT Press, 1996
C. Reeves and J. Rowe, Genetic Algorithms 
Principles and Perspectives, Kluwer Academic Publishers, 2003
J. Spall,
Introduction to Stochastic Search and
Optimization, John Wiley & Sons, 2003
A. Zalzala and P. Fleming, Genetic Algorithms in Engineering
Systems, The Institution of Electrical Engineers, 1997
J. Zurada, R.
Marks, C. Robinson, Computational
Intelligence Imitating Life, IEEE Press, 1994
Journals:
IEEE
Transactions on Evolutionary Computation
Machine
Learning
Complex Systems
Complexity International
Evolutionary
Computation
Genetic
Programming and Evolvable Machines
Web sites:
http://www.geneticprogramming.org/

John Koza’s web site
http://www.aaai.org/home.html 
The Association for the Advancement of Artificial Intelligence
http://www.alife.org/ 
International Society of Artificial Life
http://www.eleceng.ohiostate.edu/~passino/ICbook/ic_code.html

Kevin Passino’s Matlabbased GA software
http://www.cse.dmu.ac.uk/~rij/gafaq/top.htm

The HitchHiker’s Guide to Evolutionary
Computation
http://neo.lcc.uma.es/
 Networking and Emerging Optimization,
University of Málaga, Málaga, Spain
http://www.jhuapl.edu/SPSA/ 
Simultaneous Perturbation Stochastic Approximation
Prereqs:
Graduate Standing
Proficiency in Matlab
programming
Permission of instructor
Time: 4:005:50, T Th
Instructor: Dr. Dan Simon
Phone: 
2166875407 
Web Site: 

Office: 
Stilwell Hall 343 
Lab: 
Stilwell Hall 310 
Office Hours: 
3:004:00, T Th 
Feel free to email, call, or stop by my office any time and I’ll be happy
to help you if I’m available.
Grading: 

EEC 693 
EEC 793 

Homework 
25% 
20% 

Midterm 
25% 
20% 

Project 
25% 
20% 

Final 
25% 
20% 

Paper/Lecture 
 
20% 
EEC 793 students are required to write a technical paper (in addition to their project) developing some theoretical aspect of a populationbased algorithm. Part of this assignment includes presenting their results to the class in a lecturestyle presentation at a suitable time during the semester. EEC 693 students are not required to complete this assignment, although they can choose to do so for extra credit.
Homework: In addition to written exercises, Matlab assignments will be given to demonstrate the theory in the text. You can work with others on homework, but identical homework assignments will be given a grade of zero. Late homework will not be accepted. Homework should be neat, the pages should be stapled with one staple in the upper left corner, and the problems should be in order. Homework assignments and due dates are given at http://academic.csuohio.edu/simond/courses/eec693b/homework.html.
Tests: Quizzes and Exams are openbook and opennotes, but no electronic devices are allowed. No makeup quizzes or exams are allowed without the prior permission of the instructor.
Schedule:
Week
# 
Lecture
Topic 
1

Life
and Intelligence 
2 
Group
Intelligence 
3 
Genetic
Algorithms 
4 
Genetic
Algorithm Extensions 
5 
Genetic
Algorithm Analyses 
6 
Evolutionary
Computation 
7 
Cultural
Algorithms 
8 
Particle
Swarm Optimization 
9 
Ant
Colony Optimization 
10 
BiogeographyBased
Optimization 
11 
Differential
Evolution 
12 
Simulated
Annealing 
13 
Probability
Based Incremental Learning 
14 
Evolutionary
Strategies 
15 
Artificial
Immune Systems 
Project:
Each student will be responsible for a research project based on genetic
algorithms, evolutionary programming, or a related topic. The project can
involve one of a number of different problems, such as:
 The application of a populationbased
optimization algorithm to some realistic problem
 A theoretical enhancement
of some aspect of a populationbased optimization algorithm
 The study and
analysis of a journal or conference paper
 A review and analysis of early
work in populationbased optimization
 Analysis of the effects of various
parameters or options on optimization performance
 Novel approaches to
optimization (e.g., simulations of the evolution of economic, governmental, or
stellar systems)
 Some other topic related to populationbased
optimization
In all cases the project should involve the writing of software
and the presentation of simulation results. The project will be graded on the
basis of a written report and an oral report given on the last day of class. Appendix
D of Michalewicz’s book (see the reference list above) has a lot of good project
ideas and guidance.
More information about the project, including
deadlines and requirements, is at http://academic.csuohio.edu/simond/courses/eec693b/project.html.
Important
Dates:
Date 
Event 
Thurs. Oct. 9 
Midterm 
Thurs. Oct. 9 
Letter of Intent due 
Thurs. Oct. 16 
Project proposals due 
Thurs. Dec. 4 
Project presentations 
Tues. Dec. 9 
Written projects due 
Tues. Dec. 9 
Final Exam 
Homework due dates and exam dates will be
determined by the instructor during the semester and announced in class. It is
the students’ responsibility to make sure they are aware of these dates. Late
project reports will not be accepted.
Grading
Scale:
A 
93–100 
A minus 
90–92 
B plus 
87–89 
B 
83–86 
B minus 
80–82 
C 
70–79 
D 
60–69 
Department of Electrical and Computer Engineering
Last Revised: August 11, 2008