演化計算
Evolutionary Computation
| 節 | 週四 |
|---|---|
3 10:10–11:00 | 演化計算 ED102（光復） 2 節連堂 |
4 11:10–12:00 |
* 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.
無備註
Course format: Lectures (synchronous & asynchronous), student individual and 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 lecture 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
Background and objectives of evolutionary computation
Major fields of evolutionary computation
Evolutionary algorithms in general, Genetic algorithms, Evolution strategies, Evolutionary programming, Genetic programming
Other fields of evolutionary computation
Classifier systems, Coevolution, Interactive evolutionary algorithms
Advanced topics of evolutionary computation
Hybridization, Parameter control, Multimodal and multiobjectives, Constraint handling
Term project presentation
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to this courseIntroduction to Evolutionary Computation 2023-09-14(四) |
| 第 2 週 | Overview of Evolutionary Algorithms 2023-09-21(四) |
| 第 3 週 | Genetic Algorithms: Basics, Issues, and Advances 2023-09-28(四) |
| 第 4 週 | Genetic Programming 2023-10-05(四) |
| 第 5 週 | Evolution Strategies 2023-10-12(四) |
| 第 6 週 | Evolutionary Programming 2023-10-19(四) |
| 第 7 週 | Learning Classifier Systems 2023-10-26(四) |
| 第 8 週 | Other systems and algorithms 2023-11-02(四) |
| 第 9 週 | Parameter Control in Evolutionary Algorithms 2023-11-09(四) |
| 第 10 週 | Constraint HandlingHybridization with other techniques 2023-11-16(四) |
| 第 11 週 | Special Forms of Evolution 2023-11-23(四) |
| 第 12 週 | Working with Evolutionary Algorithms 2023-11-30(四) |
| 第 13 週 | Term project presentation 2023-12-07(四) |
| 第 14 週 | 2023-12-14(四) |
| 第 15 週 | 2023-12-21(四) |
| 第 16 週 | 2023-12-28(四) |
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
- R5 (by appointment only)
- 聯絡方式
- 校內分機 31446