Cleveland State University
Department of Electrical Engineering and Computer Science
EEC 693/793, ESC 794
Special Topics: Evolutionary Optimization Algorithms,Fall
Catalog Data:
Evolutionary Optimization Algorithms (4 credit hours)
Prerequisites:Graduate Standing
Proficiency in Matlab programming
Permission of instructor
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, biogeography-based optimization,
estimation of distribution algorithms, and differential evolution. Students will write
computer-based simulations of optimization algorithms using Matlab. After taking
this course the student will be able to apply population-based algorithms using
Matlab (or some other high level programming language) to realistic engineering
problems. This course will make the student aware of the current state-of-the-art 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,
Addison-Wesley, 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. El-Sharkawi, 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
Instructor:
Grading:
Dan Simon
Phone:
216-687-5407
Web Site:
http://academic.csuohio.edu/simond/
Office:
FH 343
Lab:
FH 310
Office Hours:
M W 2:30-4:00
Feel free to email, call, or stop by my office any time and I’ll be happy to help you
if I’m available.
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.
Grading Scale:
A
A minus
B plus
B
B minus
C
F
93–100
90–93
87–90
83–87
80–83
70–80
0-70
Course Outline:
Week #
1
2
3
4
5
6
7
8
9
10
11
12
13
14-15
Lecture Topic
Optimization (Chapters 1 and 2)
Genetic Algorithms (Chapter 3)
Evolutionary Programming (Chapter 5)
Evolutionary Strategies (Chapter 6)
Genetic Programming (Chapter 7)
Evolutionary Algorithm Variations (Chapter 8)
Performance Testing (Appendices B and C)
Student Lectures
Particle Swarm Optimization (Chapter 11)
Estimation of Distribution (Chapter 13)
Biogeography-Based Optimization (Chapter 14)
Other Evolutionary Algorithms (Chapter 17)
Combinatoral Optimization (Chapter 18)
Student Lectures
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 open-book and open-notes, 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.
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
- Other topics as agreed upon by the student and instructor
algorithms
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.