COE 589: Special Topics in Computer Systems and Applications:
Evolutionary Computation (3-0-3)
Term 101 (Fall 2010-11)
Syllabus
Section: 01 Time: SM 20:00-21:15 Place: 24-104
Instructor: Dr. Zubair Ahmed Baig Office: Bldg 22/ Room 124-8
Phone: 860-7548 Email: zbaig@kfupm.edu.sa
Office Hours: TBA
Web site: http://faculty.kfupm.edu.sa/COE/zbaig
Course Material: All course material will be posted on WebCT, and not on my personal website
Course Description:
Introduction to the fundamental principles and practices underlying the field of evolutionary computation. Application of evolutionary algorithms to various optimization problems in engineering. Hybridization of evolutionary computing techniques with other disciplines such as Fuzzy logic, Neural Networks etc. Design and Modeling of engineering solutions based on the principles of evolutionary algorithms.
Course Prerequisite: Graduate Standing
Course Materials:
No prescribed text book will exist for the course. A few reference books along with handouts will be used. A non-exhaustive list of reference books is as follows:
Kenneth A. De Jong, “Evolutionary Computation: A Unified Approach,” 2006, MIT Press.
Ed. Bäck, Fogel and Michalewicz, "Evolutionary Computation1: Basic Algorithms and Operators,” 2000, CRC Press.
Daniel Ashlock, “Evolutionary Computation for Modeling and Optimization,” 2006, Springer Press.
Grade Distribution:
Short Assignments: 15%
Term Project (Group-based): 25%
Research Paper Review/Presentations: 15%
Midterm Exam: 20% (During Week 9)
Final Exam: 25%
Goals:
1. To introduce students to the class of algorithms constituting evolutionary computation
2. To define and introduce the application of evolutionary computation in various disciplines, with a
focus on computing problems (such as network optimization, tradeoff, evolutionary logic functions, nano-system design etc.)
3. To model solutions to optimization and engineering design problems based on the concepts of evolutionary computing.
4. To study and analysis the latest evolutionary techniques (including hybrid algorithms) for solving engineering problems.
5. To train students on implementation of evolutionary computation solutions thru assignments and a course project.
Course Topics and Weekly Breakdown:
|
Week |
Topic |
|
|
|
||
|
1 |
Introduction: Fundamentals of Evolutionary Algorithms |
|
|
2-3 |
Genetic Algorithms: Operators, Search Parameters, Techniques |
|
|
4 |
Genetic Programming |
|
|
5 |
Applications to Engineering Design |
|
|
6 |
Nano-Applications and Case Studies |
|
|
7 |
Hybridization with Artificial Neural Networks |
|
|
8 |
Evolutionary Computing and Machine Learning |
|
|
9 |
Evolutionary Logic Functions |
|
|
10 |
Swarm Intelligence |
|
|
11 |
Multi-objective and hybrid evolutionary algorithms |
|
|
12 |
Evolutionary Computing for Computer Network Optimization |
|
|
13 |
Research Paper Review/Presentations |
|
|
14 |
Project Presentations |
|
|
15 |
Project Presentations |
|
Note: The 1st 12 weeks will be more teaching-centered, wherein the students will be exposed to the area of Evolutionary computation with real-life application scenarios. During this period, the students will be provided with a set of short assignments. Some of these assignments will be research-based, and some will be programming-based.
The last 3 weeks will be devoted towards student contribution in the form of a group project on an application of choice in the area of Evolutionary computation, and project presentations.