Cleveland State University

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, 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, McGraw-Hill, 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 4:00-5:50

 

Instructor:         Dan Simon

Phone:

216-687-5407

Web Site:

http://academic.csuohio.edu/simond/

Office:

Stilwell Hall 343

Lab:

Stilwell Hall 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.

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 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.

 

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

Biogeography-Based Optimization (Chapter 14)

12

Other Evolutionary Algorithms (Chapter 17)

13

Combinatoral Optimization (Chapter 18)

14-15

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

0-70

 


Professor Simon’s Home Page

Department of Electrical and Computer Engineering

Cleveland State University


Last Revised: August 24, 2012