Department of Electrical and
Computer Engineering
EEC
693/793, ESC 794
Special
Topics: Population-Based Optimization (4 credit hours)
Fall
2008
Description: This course discusses the theory, history, mathematics, and applications of population-based 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, biogeography-based optimization, differentical evolution, and artificial immune systems. 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:
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, 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
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.genetic-programming.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.ohio-state.edu/~passino/ICbook/ic_code.html
-
Kevin Passino’s Matlab-based GA software
http://www.cse.dmu.ac.uk/~rij/gafaq/top.htm
-
The Hitch-Hiker’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: 4:00-5:50, T Th
Instructor: Dr. Dan Simon
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Phone: |
216-687-5407 |
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Web Site: |
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Office: |
Stilwell Hall 343 |
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Lab: |
Stilwell Hall 310 |
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Office Hours: |
3:00-4:00, 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.
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Grading: |
|
EEC 693 |
EEC 793 |
|
|
Homework |
25% |
20% |
|
|
Midterm |
25% |
20% |
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Project |
25% |
20% |
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Final |
25% |
20% |
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Paper/Lecture |
- |
20% |
EEC 793 students are required to write a technical paper (in addition to their project) developing some theoretical aspect of a population-based algorithm. Part of this assignment includes presenting their results to the class in a lecture-style 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 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.
Schedule:
|
Week
# |
Lecture
Topic |
|
1
|
Life
and Intelligence |
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2 |
Group
Intelligence |
|
3 |
Genetic
Algorithms |
|
4 |
Genetic
Algorithm Extensions |
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5 |
Genetic
Algorithm Analyses |
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6 |
Evolutionary
Computation |
|
7 |
Cultural
Algorithms |
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8 |
Particle
Swarm Optimization |
|
9 |
Ant
Colony Optimization |
|
10 |
Biogeography-Based
Optimization |
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11 |
Differential
Evolution |
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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 population-based
optimization algorithm to some realistic problem
- A theoretical enhancement
of some aspect of a population-based optimization algorithm
- The study and
analysis of a journal or conference paper
- A review and analysis of early
work in population-based 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 population-based
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 |
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Thurs. Dec. 4 |
Project presentations |
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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 |
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A minus |
90–92 |
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B plus |
87–89 |
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B |
83–86 |
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B minus |
80–82 |
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C |
70–79 |
|
D |
60–69 |
Department of Electrical and Computer Engineering
Last Revised: August 11, 2008