| 一、教學目標 |
Understand fundamental concept and mathematical theory of
optimization under multiple objectives.
Learn how to use evolutionary algorithms to solve
the multi-objective optimization problems,
and its applications in business and management. |
| 二、先修科目 |
Operations Research,
Probability and Statistics,
and Algorithms |
| 三、教材內容 |
Deb, K. (2001), Multiobjective Optimization using
Evolutionary Algorithms. New York: John Wiley & Sons.
|
| 四、教學方式 |
Lecture, discussion, and presentation. |
| 五、參考書籍 |
Yoon, K.P., Hwang, C.-L. (1995), Multiple Attribute
Decision Making: An Introduction. California: SAGE. |
| 六、教學進度 |
(SCHOOL: Professional disposition, international vision;
DEPARTMENT: Expertise in information management, practical
learning, innovative research, international vision)
Topic 1 Prologue
Topic 2 Multi-objective optimization
Topic 3 Classical methods for multi-objective optimization
Topic 4 Evolutionary algorithms
Mid-term Exam.
Topic 5 Nonelistist multi-objective evolutionary algorithms
Topic 6 Elistist multi-objective evolutionary algorithms
Topic 7 Constrained multi-objective evolutionary algorithms
Topic 8 Salient issues multi-objective evolutionary algorithms
Topic 9 Applications of multi-objective evolutionary algorithms
Topic 10 Presentations about Multiple Attributes Decision Analysis
(Chapters extracted from Yoon and Hwang, 1995)
Final Exam.
(This syllabus is subject to change in case of achieving the
desired quality of the course.) |
| 七、評量方式 |
Attendance & discussion 10%, term project (R scripting) 30%,
mid-term exam. 30%, final exam. 30% |
| 八、講義位址 |
|
