Department
of Electrical and Computer Engineering
EEC
693/793, ESC 794
Special
Topics: Evolutionary Optimization Algorithms (4 credit hours)
Fall
2012
Description: This course discusses the theory, history, mathematics, and applications of evolutionary optimization algorithms, most of which are based on biological processes. Some of the algorithms that may be covered include genetic algorithms, evolutionary programming, evolutionary strategies, genetic programming, particle swarm optimization, ant colony optimization, biogeographybased optimization, estimation of distribution algorithms, and differential evolution. 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: D.
Simon, Evolutionary Optimization
Algorithms, John Wiley & Sons, 2013.
Purchase a draft of the text from the instructor for $35 (cash or check).
Web page: http://academic.csuohio.edu/simond/EvolutionaryOptimization
References: T.
Back, Evolutionary Algorithms in Theory
and Practice, Oxford University Press, 1996
M. Batty, Cities and Complexity, MIT
Press, 2005
E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm
Intelligence, Oxford University Press, 1999
M. Clerc, Particle Sarm Optimization,
ISTE Ltd., 2006
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
R. Eberhart, Y. Shi, and J. Kennedy, Swarm
Intelligence, Morgan Kaufmann, 2001
A. Engelbrecht, Computational
Intelligence, John Wiley & Sons, 2007
A. Engelbrecht, Fundamentals of
Computational Swarm Intelligence, John Wiley & Sons, 2005
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
K. Lee and M. ElSharkawi, Modern
Heuristic Optimization Techniques, John Wiley & Sons, 2008
Z. Michalewicz and D. Fogel, How To Solve
It, Springer, 2000
Z. Michalewicz, Genetic Algorithms + Data
Structures = Evolution Programs, Springer, 1996
M. Minsky, The Society of Mind, Simon
& Schuster, 1985
M. Mitchell, An Introduction to Genetic
Algorithms, MIT Press, 1996
C. Reeves, Modern Heuristic Techniques for Combinatorial Problems,
McGrawHill, 1995
C. Reeves and J. Rowe, Genetic
Algorithms  Principles and
Perspectives, Kluwer Academic Publishers, 2003
T. Segaran, Programming Collective
Intelligence: Building Smart Web 2.0 Applications, O’Reilly, 2007
J. Spall, Introduction to Stochastic
Search and Optimization, John Wiley & Sons, 2003
M. Vose, The Simple Genetic Algorithm,
MIT Press, 1999
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
Swarm
Intelligence
Evolutionary
Intelligence
Applied Soft
Computing
Swarm
and Evolutionary Computation
Prereqs: Graduate Standing
Proficiency in Matlab programming
Permission of instructor
Time: M W 4:005:50
Instructor: Dan Simon
Phone: 
2166875407 
Web Site: 

Office: 
Stilwell Hall 343 
Lab: 
Stilwell Hall 310 
Office Hours: 
M W 2:304:00 
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: 

Masters 
Doctoral 

Homework 
25% 
20% 

Midterm 
25% 
20% 

Lectures 
25% 
20% 

Final Exam 
25% 
20% 

Paper 
 
20% 
Doctoral students are required to write a technical paper for journal or conference submission, and to present their research to the class. Masters 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.
Tests: 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. The final exam is Monday, December 10.
Approximate Schedule:
Week # 
Lecture
Topic 
1 
Optimization
(Chapters 1 and 2) 
2 
Genetic Algorithms (Chapter 3) 
3 
Evolutionary Programming (Chapter 5) 
4 
Evolutionary Strategies (Chapter 6) 
5 
Genetic Programming (Chapter 7) 
6 
Evolutionary Algorithm Variations
(Chapter 8) 
7 
Performance Testing (Appendices B and C) 
8 
Student Lectures 
9 
Particle Swarm Optimization (Chapter 11) 
10 
Estimation of Distribution (Chapter 13) 
11 
BiogeographyBased Optimization (Chapter
14) 
12 
Other Evolutionary Algorithms (Chapter
17) 
13 
Combinatoral Optimization (Chapter 18) 
1415 
Student Lectures 
Student Lectures:
Each student will be responsible for preparing and delivering
several lectures to the class based on individual study. Possible lecture
topics include:
 The application of an
evolutionary optimization algorithm to some realistic problem
 The theoretical enhancement or extension of an evolutionary optimization
algorithm
 The study and analysis of a journal or conference paper
 A review and analysis of early historical work in evolutionary optimization
 Investigation or analysis of the effects of various tuning parameters or
options on evolutionary optimization performance
 Novel approaches to evolutionary optimization (e.g., simulations of the
evolution of economic, governmental, or stellar systems)
 Discussion and demonstration of one of the book topics not covered by the
instructor
 Hybridization of two or more evolutionary optimization
algorithms
 Other topics as agreed upon by the student and instructor
Lecture grades will be given on the basis of technical rigor, demonstrable results, level of interest, organization, and creativity.
Important Dates:
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.
Grading Scale:
A 
93–100 
A minus 
90–93 
B plus 
87–90 
B 
83–87 
B minus 
80–83 
C 
70–80 
F 
070 
Department of Electrical and Computer Engineering
Last Revised: August 24, 2012