Cleveland State University

Department of Electrical and Computer Engineering

 

EEC 693 / 793

Special Topics: Genetic Algorithms

Summer 2004

 

Description:    Computer-based simulations of GAs (genetic algorithms) for optimization and engineering problems. After taking this course, the student should be able to implement a GA in Matlab (or some other high level programming language), be aware of the current state of the art in the field, and be able to conduct independent research in the field.

 

Text:               David Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989

 

References:    R. Haupt and S. Haupt, Practical Genetic Algorithms, John Wiley & Sons, 1998
M. Mitchell, An Introduction to Genetic Algorithms, The MIT Press, 1996
C. Reeves and J. Rowe, Genetic Algorithms - Principles and Perspectives, Kluwer Academic Publishers, 2003
M. Jamshidi, Robust Control Systems with Genetic Algorithms, CRC Press, 2003
D. Fogel, Evolutionary Computation: The Fossil Record, IEEE Press, 1998
J. Koza, Genetic Programming, The MIT Press, 1992
D. Coley, An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific, 1999
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1996
L. Davis, Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991
J. Zurada, R. Marks, C. Robinson, Computational Intelligence Imitating Life, IEEE Press, 1994
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
A. Zalzala and P. Fleming, Genetic Algorithms in Engineering Systems, The Institution of Electrical Engineers, 1997
J. Holland, Adaptation in Natural and Artificial Systems, The MIT Press, 1992

Journals:
            IEEE Transactions on Evolutionary Computation
            Machine Learning
            Complex Systems
            Complexity International
            Evolutionary Computation
            Genetic Programming and Evolvable Machines

A search for “genetic algorithms” on the Internet leads to a lot of good web sites - for example,
http://www.genetic-programming.org/ - John Koza’s web site
http://www.aic.nrl.navy.mil/galist/ - The Genetic Algorithms Archive
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.cs.bham.ac.uk/Mirrors/ftp.de.uu.net/EC/clife/www/ - The Hitch-Hiker’s Guide to Evolutionary Computation

 

Prereqs:          Graduate Standing
Proficiency in Matlab programming

 

Time:              4:00 - 6:00  M W


Instructor:      Dr. Dan Simon

Phone:

216-687-5407

Web Site:

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

Office:

Stilwell Hall 343

Lab:

Stilwell Hall 306, 308, 326

Office Hours:

By appointment

                        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:

Quizzes

20%

 

Homework

20%

 

Matlab

20%

 

Midterm

20%

 

Project

20%

 

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. Any deviation from this format may result in a decrease of the homework grade.

 

Tests:              Quizzes and Exams will be open-book and open-notes, but no electronic devices (e.g., calculators or laptops) will be allowed. No makeup quizzes or exams will be allowed without the prior permission of the instructor.

 

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 GA to some realistic problem

·        A theoretical enhancement of some aspect of GAs or EP

·        The study and analysis of a journal or conference paper

·        A review and analysis of early work in GAs or EP

·        Analysis of the effects of various parameters / options on GA / EP performance

·        Novel approaches to GAs or EP (e.g., simulations of the evolution of economic, governmental, or stellar systems)

·        Some other topic related to GAs or EP

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 (between 10 and 20 minutes) 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.

Project grade: Each of the subtasks listed below is graded on a scale from 0 (low) to 4 (high). The written report can be based on the template at http://academic.csuohio.edu/simond/courses/Report%20Template.doc although this is not required.

 

Task

Subtask

Points

Proposal

 

16

 

Abstract

 

 

Project Description

 

 

Expected Results

 

 

Timeline

 

Project

 

24

 

Interest Level

 

 

Motivation

 

 

Features

 

 

Success

 

 

Results

 

 

Scope

 

Written Report

 

32

 

Abstract

 

 

Introduction

 

 

Software Listing

 

 

Figures and Graphics

 

 

Organization

 

 

Clarity

 

 

Conclusion

 

 

References

 

Oral Presentation

 

28

 

Introduction

 

 

Correct Length

 

 

Organization

 

 

Clarity

 

 

Visual Aids

 

 

Audience Interaction

 

 

Conclusion

 

Total

 

100

 

Important Dates:

 

Date

Event

May 31

Holiday

July 5

Holiday

August 9 - 11

Project presentations

August 11

Written projects due

 

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

 


Professor Simon's Home Page

Department of Electrical and Computer Engineering

Cleveland State University


Last Revised: June 2, 2004