| Relevance of Course Objectives and Core Learning Outcomes(%) |
Teaching and Assessment Methods for Course Objectives |
| Course Objectives |
Competency Indicators |
Ratio(%) |
Teaching Methods |
Assessment Methods |
1. Learn the foundations of Evolutionary Computation
2. Apply Python to computational EC programming problems
3. Learn advanced applications of EC |
|
|
|
| Oral Presentation |
| Written Presentation |
| Assignment |
|
| Course Content and Homework/Schedule/Tests Schedule |
| Week |
Course Content |
| Week 1 |
Class Introduction & Evolutionary Computation Overview |
| Week 2 |
Python programming & Production Software Development Best Practices |
| Week 3 |
EC Origins & Motivation |
| Week 4 |
EC Components |
| Week 5 |
EC Representation |
| Week 6 |
Selection & Population Management, EA Variants |
| Week 7 |
EC Code Architecture & Algorithm Testing |
| Week 8 |
Multi-objective & Constrained EC |
| Week 9 |
Parallel EA’s & Python multiprocessing |
| Week 10 |
Evolutionary Electronics & IC Design Automation (part 1) |
| Week 11 |
Evolutionary Electronics & IC Design Automation (part 2) |
| Week 12 |
Reinforcement Learning, Co-evolutionary EA’s, Memetic & Interactive EC |
| Week 13 |
ANN’s & Evolutionary Robotics/Things |
| Week 14 |
Example applications & code |
| Week 15 |
Presentation of Final Projects |
| Week 16 |
Presentation of Final Projects |
self-directed
learning |
Supplemental self-study lecture materials & code examples. |
|
| Evaluation |
| homework projects (65%), final project (35%) |
| Textbook & other References |
1. “Introduction to Evolutionary Computing”, 2nd Ed. A.E. Eiben and J.E. Smith, Springer, 2015
2. “Learning Python”, 5th Ed., Mark Lutz, O’Reilly, 2013 |
| Teaching Aids & Teacher's Website |
講義可於下列網頁取得：
iLearning 3.0 (http://lms2020.nchu.edu.tw/)登入後，選「演化式計算」 |
| Office Hours |
|
| Sustainable Development Goals, SDGs(Link URL) |
| include experience courses：N |
|