CSCI 4560/6560 Evolutionary Computation and Its Applications

CSCI 4560/6560 Evolutionary Computation and Its Applications

Fall 2009: Tuesdays and Thursdays 3:30pm - 4:45pm, Wednesdays 3:35pm - 4:25pm, Geog-Geol 200B

Instructor: Prof. Khaled Rasheed
Telephone: 542-3444
Office Hours: Wednesday: 4:35-6:00pm & Thursday: 12:30-1:30pm or by email appointment
Office Location: Room 219B, Boyd GSRC
Email: khaled@cs.uga.edu

Teaching Assistant: Liang Wang
Office Hours: Tuesday: 11am-noon
Office Location: Room 301A, Boyd GSRC
Email: kavenvih@uga.edu

Objectives:

To provide a broad introduction to the field of Genetic Algorithms and other fields of Evolutionary Computation and global optimization. To teach students how to apply these methods to solve problems in complex domains. The course is appropriate both for students preparing for research in Evolutionary Computation, as well as Science and Engineering students who want to apply Evolutionary Computation techniques to solve problems in their fields of study.

Recommended Background:

CSCI 2720 Data Structures (or permission of the instructor). Familiarity with basic computer algorithms and data structures and at least one high level programming language.

Topics to be Covered:

Genetic Algorithm core topics including representation, operators and architectures. Other fields of evolutionary computation including Genetic Programming, Evolution Strategies, Evolutionary Programming and Classifier Systems. Evolutionary Computation applications in science and Engineering. Other nature-inspired global optimization techniques.

Expected Work:

Reading; assignments (including programming); midterm; final; and term project and paper. (Unless otherwise announced by the instructor: all assignments and all exams must be done entirely on your own.)

Academic Honesty and Integrity:

All academic work must meet the standards contained in "A Culture of Honesty." Students are responsible for informing themselves about those standards before performing any academic work. The penalties for academic dishonesty are severe and ignorance is not an acceptable defense.

Grading Policy:

  • Assignments: 30% (Programs, questions, paper presentations)
  • Midterm Examination: 20%
  • Final Examination: 25%
  • Term Project: 25% (includes term paper and presentation)
    Students may work on their term projects individually or in groups of up to THREE students each. The above distribution is only tentative and may change later. The instructor will announce any changes.

    Assignment Submission Policy

    Assignments must be turned in by the assigned deadline. Late assignments will not be accepted. Rare exceptions may be made by the instructor only under extenuating circumstances and in accordance with the university policies.

    Course Home-page

    A variety of materials will be made available on the EC Class Home-page at http://www.cs.uga.edu/~khaled/ECcourse/, including handouts, lecture notes and assignments. Announcements may be posted between class meetings. You are responsible for being aware of whatever information is posted there.

    Lecture Notes

    Copies of some of Dr. Rasheed's lecture notes will be available at the bottom of the class home page. Not all the lectures will have electronic notes though and the students should be prepared to take notes inside the lecture at any time.

    Textbook in Bookstore

  • "Introduction to Evolutionary Computing", Eiben and Smith. Springer-Verlag, New York, 2003. (Required)

    Additional Books

  • "Genetic Algorithms in Search, Optimization, and Machine Learning", David Goldberg. Addison-Wesley, 1989.
  • "An Introduction to Genetic Algorithms", Melanie Mitchell. MIT Press, 1996.
  • "Genetic Algorithms + Data Structures = Evolution Programs", Zbigniew Michalewicz. Springer-Verlag, New York,1996.
  • "Evolutionary Computation", D. Dumitrescu et al. CRC Press, 2000.
  • "Evolutionary Computation 1", Thomas Back et al. IOP Publishing, 2000.
  • "Evolutionary Computation, A "Unified Approach", K. DeJong. MIT Press, 2006.

    Web Resources

  • GA software packages from the CMU Artificial Intelligence repository
  • Web archived version of the GA source codes at GALIST archives

    Announcements:

  • [12-3-2009]: The project report should look like a conference paper of about 8 two-column pages in small font or 20 single-column pages in large font (there is no restiction on size though). You should include an introduction, a mention of related work, a description of your experiments and results and a conclusion which may include limitations and/or future work. For application oriented projects, don't forget to describe the domain that you applied your EC technique(s) to, in enough detail for the reader to appreciate the significance and difficulty of the problem. The reports are due during the final exam. Please bring a hard copy to the final.
  • [12-3-2009] The final will be 3:30-6:30pm on Tuesday December 15th. It will be in the same classroom in which the lectures met. It will cover all the topics discussed in the course. It will be open book and notes but no laptops will be allowed. You should bring a calculator to the exam. You should also bring your own lecture notes. One of the questions will be about a paper presented in class. Copies of that paper will be provided for you in the exam so you need not print them.

    Papers:

  • "An empirical study on GAs without parameters" Thomas Back et al., 2000. [Ananta Palani][10-21]{download}
  • "A Genetic Programming Approach to Automated Software Repair" Stephanie Forrest et al., 2009. [Brett Meyer][10-28]{download}
  • "Performance Scaling of Multi-objective Evolutionary Algorithms" V. Khare et al., 2003. [Soumya][11-4]{download}
  • "Differential Evolution for Discrete Optimization: An Experimental Study on Combinatorial Auction Problems" Jingqiao Zhang, et al., 2008. [Matthew Tanner][11-10]{download}
  • "Multiplicative Approximations and the Hypervolume Indicator" Tobias Friedrich, et al., 2009. [Bernhard][11-17]{download}
  • "Using Evolutionary Techniques to Hunt for Snakes and Coils" Casella, D.A., and Potter, W.D., 2005. [David][11-17] {download}
  • "Immunity by Design: An Artificial Immune System" Steven A. Hofmeyr and Stephanie Forrest,1999.[Gavin][11-17] {download}
  • "The Suitability of Genetic Algorithms for Musical Composition" Andrew Gartland-Jones and Peter Copley, 2003. [Michael Plunkett][11-17] {download}
  • "BOA: The Bayesian optimization algorithm". Martin Pelikan et al.,1999. [Yassin Nachite][11-18] {download}
  • "Ant Colony Optimization for dynamic Traveling Salesman Problems" Carlos A. Silva and Thomas A. Runkler, 2000. [Mohammedali][11-18]{download}
  • "String- and Permutation-Coded Genetic Algorithms for the Static Weapon-Target Assignment Problem" Julstrom, 2009. [Nithya][11-19] {download}
  • "Motion Planning for a Robot Arm by Using Genetic Algorithm" Bahaa Ibraheem Kazem et al., 2008.[Kaza][11-19] {download}
  • "Evolving an Expert Checkers Playing Program without Using Human Expertise" Kumar Chellapilla and David Fogel, 2001. [Adam][11-19] {download}
  • "Automatic design and manufacture of robotic lifeforms" Hod Lipson & Jordan B. Pollack, 2000. [Will][11-19] {download}
  • "The generation of form using an evolutionary approach" Mike Rosenman,1997.[?][?]{download}
  • "Multiobjective optimization using cooperative coevolution" K. Maneeratana et al., 2004. [?][?]{download}

    Assignments:

  • Homework 1
  • Homework 2
  • Homework 3
  • Homework 4
  • Homework 5
  • Homework 6

    Lecture Notes:

  • Introduction
  • Chapter 1
  • Chapter 2
  • Chapter 3
  • Chapter 4
  • Chapter 5
  • Chapter 6
  • Chapter 8
  • Chapter 9
  • GADO
  • Chapter 10
  • Chapter 14
  • Chapter 11
    The course syllabus is a general plan for the course; deviations announced to the class by the instructor may be necessary.
    Last modified: December 3, 2009.
    Khaled Rasheed (khaled (at) cs.uga.edu)