演化計算
Evolutionary Computation
| 節 | 週五 |
|---|---|
3 10:10–11:00 | 演化計算 ED202 2 節連堂 |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
※ Manual course add is unavailable for this course. 本課程不開放手動加選。 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 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 project presentation and individual/group discussion. Course website: NYCU E3 platform
※ Manual course add is unavailable for this course. 本課程不開放手動加選。 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. Asynchronous activities participation: 20% 2. Homework: 25% 2-1. Goal statement: 20% (of 25%) 2-2. Homework #1: 40% (of 25%) 2-3. Homework #2: 40% (of 25%) 3. Final: 20% 4. Term project: 35% 4-1. Proposal: 20% (of 35%) 4-2. Progress report: 15% (of 35%) 4-3. Presentation: 30% (of 35%) 4-4. Final report: 35% (of 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 courseIntroduction to Evolutionary Computation |
| 第 2 週 | Overview of Evolutionary Algorithms |
| 第 3 週 | Genetic Algorithms: Basics, Issues, and Advances |
| 第 4 週 | Genetic Programming |
| 第 5 週 | Evolution Strategies |
| 第 6 週 | Evolutionary Programming |
| 第 7 週 | Learning Classifier Systems |
| 第 8 週 | Other systems and algorithms |
| 第 9 週 | Parameter Control in Evolutionary Algorithms |
| 第 10 週 | Constraint HandlingHybridization with other techniques |
| 第 11 週 | Special Forms of Evolution |
| 第 12 週 | Working with Evolutionary Algorithms |
| 第 13 週 | Term project presentation |
| 第 14 週 | |
| 第 15 週 | |
| 第 16 週 |
No required textbook. The following are reference books: Introduction to Evolutionary Computing, A. E. Eiben, J. E. Smith, Agoston E. Eiben, J. D. Smith, Springer-Verlag, 2003. ISBN: 3540401849. An Introduction to Genetic Algorithms for Scientists and Engineers, David A. Coley, World Scientific Publishing Company, 1997. ISBN: 9810236026. Handbook of Evolutionary Computation, Thomas Baeck, David B Fogel, Zbigniew Michalewicz, Institute of Physics Publishing, 2003. ISBN: 0750308958. Genetic Algorithms in Search, Optimization, and Machine Learning, David E. Goldberg, Addison-Wesley Pub Co, 1989. ISBN: 0201157675. The Design of Innovation: Lessons from and for Competent Genetic Algorithms, David E. Goldberg, Kluwer Academic Publishers, 2002. ISBN: 1402070985. ※ 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
- F5 (by appointment only)
- 聯絡方式
- ypchen@cs.nycu.edu.tw