Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2026 356919 Evolutionary Computing 1 3.0 3 ★ Tsai Men-Shen GAT 8 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	356919	Evolutionary Computing	1	3.0	3	★	Tsai Men-Shen	GAT	8	0

Course Plan：

Instructor Tsai Men-Shen　Office Hours

Email mstsai@ntut.edu.tw

Last Updated 2025-12-18 08:00:39

Course Objective 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.

Course Schedule 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

Evaluation and grading policy 1. Home Work 30% 2. Midterm Report 20% 3. Midterm Exam 20% 4. Final Project/Report 20% 5. Participation/popup-quiz 10%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
Mitsuo Gen and Runwei Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., 1997. Selected technical papers.

The access to curricular consultation Friday afternoon 14:00-17:00, or by appointment.

Expanding teaching and resources

The course corresponds to the SDGs SDG4：優質教育（Quality Education）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）

Does the course incorporate AI ● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools）
● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Tsai Men-Shen　Office Hours
Email	mstsai@ntut.edu.tw
Last Updated	2025-12-18 08:00:39
Course Objective	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.
Course Schedule	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
Evaluation and grading policy	1. Home Work 30% 2. Midterm Report 20% 3. Midterm Exam 20% 4. Final Project/Report 20% 5. Participation/popup-quiz 10%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes Mitsuo Gen and Runwei Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., 1997. Selected technical papers.
The access to curricular consultation	Friday afternoon 14:00-17:00, or by appointment.
Expanding teaching and resources
The course corresponds to the SDGs	SDG4：優質教育（Quality Education） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities）
Does the course incorporate AI	● 鼓勵學生使用生成式AI工具（Encourage students to use generative AI tools） ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note