教學大綱與進度

課程基本資料：

學年期	課號	課程名稱	階段	學分	時數	修	教師	班級	人	撤	備註
111-2	314363	演化式計算	1	3.0	3	★	蔡孟伸	自動化所	16	2

教學大綱與進度：

教師姓名	蔡孟伸　教師諮商時間(Office Hours)
Email	mstsai@ntut.edu.tw
最後更新時間	2022-12-20 12:57:51
課程大綱	This course presents analytic concepts in soft computing techniques. It is designed for the beginning graduate students who are interesting in application of soft computing techniques to engineer problems. Students should have a mathematical maturity typical of undergraduate curricula in science and engineering, including calculus and discrete mathematics. The teaching approach in this course emphasizes modeling, analysis and design principles of soft computing techniques. The subject material includes genetic algorithm, Ant Colony Optimization and Particle Swarm Optimization. Case studies include several engineering applications.
課程進度	1 Administrative/Introduction 2 Fundamental of Genetic Algorithm 3 Fundamental of Genetic Algorithm HW#1 4 Constrained Optimization Problem 5 HW#1 Presentation HW#1 Due 6 Constrained Optimization Problem HW#2 7 Combinatorial Optimization Problems 8 HW#2 Presentation HW#2 Due 9 Combinatorial Optimization Problems 10 Combinatorial Optimization Problems 11 Mid-Term Exam 12 Mid-Term Report Presentation 13 Flow-Shop Sequence Problems Final project proposal 14 Flow-Shop Sequence Problems 15 Job-Shop Scheduling Class Evaluation 16 ACO/PSO 17 Final Project Presentation 18 Final Exam Final Project Report Due
評量方式與標準	1. Home Work 30% 2. Midterm Report 20% 3. Midterm Exam 20% 4. Final Project/Report 20% 5. Participation/popup-quiz 10%
使用教材、參考書目或其他	【遵守智慧財產權觀念，請使用正版教科書，不得使用非法影印教科書】 使用外文原文書：是 Mitsuo Gen and Runwei Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., 1997. Selected technical papers.
課程諮詢管道	Friday afternoon 14:00-17:00, or by appointment.
備註	電子郵件：mstsai@mail.ntut.edu.tw 如因疫情需改為線上上課時，則使用Teams。