演化計算
Evolutionary Computation
| 節 | 週一 | 週四 |
|---|---|---|
3 10:10–11:00 | 演化計算 ED302 2 節連堂 | |
4 11:10–12:00 | ||
7 15:30–16:20 | 演化計算 ED302 |
* 根據陽明交大上課時間表所列
The goal of this course is to introduce the background, objectives, current development, and possible future directions of evolutionary computation in a broad sense. It covers all major fields of evolutionary computation, including genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. Certain advanced topics, such as hybridization, constraint handling, interactivity, etc., are also included in the course.
Fundamental programming capability.
Course format: Lectures, student individual and group discussion. Course website: NCTU NewE3 platform
本課程以百分考評 作業部份: Homework: Implementations of simple and straightforward genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. Term project: Teamwork project, peer-review project report, and report presentation. Grading Policy: 1. Homework: 30% 1-1. Goal statement (1/3) 1-2. Homework #1 (1/3) 1-3. Homework #2 (1/3) 2. Final: 25% 3. Term project: 45% 3-1. Proposal (20%) 3-2. Progress report (15%) 3-3. Presentation (30%) 3-4. Final report (35%)
- Introduction
- Major fields of evolutionary computation
- Other fields of evolutionary computation
- Advanced topics of evolutionary computation
- Term project presentation
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to this course Introduction to Evolutionary Computation |
| 第 2 週 | Overview of Evolutionary Algorithms |
| 第 3 週 | Genetic Algorithms: Basics |
| 第 4 週 | Genetic Algorithms: Basics |
| 第 5 週 | Genetic Algorithms: Issues and Advances |
| 第 6 週 | Genetic Programming |
| 第 7 週 | Evolution Strategies |
| 第 8 週 | Evolutionary Programming |
| 第 9 週 | Learning Classifier Systems Mid-term examination |
| 第 10 週 | Other systems and algorithms |
| 第 11 週 | Parameter Control in Evolutionary Algorithms |
| 第 12 週 | Constraint Handling Hybridization with other techniques |
| 第 13 週 | Special Forms of Evolution |
| 第 14 週 | Working with Evolutionary Algorithms |
| 第 15 週 | Term project presentation |
| 第 16 週 | Term project presentation |
| 第 17 週 | Term project presentation |
No required textbook. The following are reference books: <OL> <LI><I>Introduction to Evolutionary Computing</I>, A. E. Eiben, J. E. Smith, Agoston E. Eiben, J. D. Smith, Springer-Verlag, 2003. ISBN: 3540401849.</LI> <LI><I>An Introduction to Genetic Algorithms for Scientists and Engineers</I>, David A. Coley, World Scientific Publishing Company, 1997. ISBN: 9810236026.</LI> <LI><I>Handbook of Evolutionary Computation</I>, Thomas Baeck, David B Fogel, Zbigniew Michalewicz, Institute of Physics Publishing, 2003. ISBN: 0750308958.</LI> <LI><I>Genetic Algorithms in Search, Optimization, and Machine Learning</I>, David E. Goldberg, Addison-Wesley Pub Co, 1989. ISBN: 0201157675.</LI> <LI><I>The Design of Innovation: Lessons from and for Competent Genetic Algorithms</I>, David E. Goldberg, Kluwer Academic Publishers, 2002. ISBN: 1402070985.</LI> </OL> ※請修課同學尊重智慧財產權!勿隨意過度影印教科書或使用未經授權之著作權與電腦軟體等。
- 地點
- EC711
- 時間
- 4E (by appointment)
- 聯絡方式
- 校內分機 31446