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 359552 Intelligent Evolutionary Computation 1 3.0 3 ★ Cheng-Yen Chiang MSAI 27 0 　

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2026	359552	Intelligent Evolutionary Computation	1	3.0	3	★	Cheng-Yen Chiang	MSAI	27	0

Course Plan：

Instructor Cheng-Yen Chiang　Office Hours

Email dcychiang@ntut.edu.tw

Last Updated 2025-12-19 23:17:55

Course Objective 自然界的進化行為一直人類賦予計算機智慧學習的對象，但仿效自然界中生物間的適應性過程，建構出的龐大模型，一直是在一項艱鉅的挑戰，但不諱言的他的回報也是巨大的。進化計算便是基於生物遺傳學、自然選擇和出現的適應性行為等進化機制，建立出來的智慧型模型，以利智慧系統的建立。本課程將介紹各項演化計算方式，並以實作的方式實際了解彼此間的差異。 The evolutionary behavior of nature has always been the object of learning that man has endowed computer intelligence with. However, the construction of huge models that emulate the adaptive processes among organisms in nature has been a difficult challenge, but the rewards have been enormous. Evolutionary computation is an intelligent model based on evolutionary mechanisms such as biological genetics, natural selection, and emergent adaptive behaviors to facilitate the establishment of intelligent systems. This course will introduce the various evolutionary computation methods and provide a practical understanding of the differences between them in a hands-on manner.

Course Schedule 1. Introduction of evolutionary algorithm 2. Simple Genetic Algorithm 3. Evolution Strategy 4. Evolutionary Programming 5. Genetic Programming 6. Direction based Search 7. Implementation Encoding and Operators 8. Selection Methods 9. Replacement and Stop Criteria 10. Parameter Control 11. Performance Evaluation 12. Ant Colony Optimization 13. Particle Swarm Optimization 14. Artificial Immune Systems

Evaluation and grading policy 作業(Assignment and Report) 60% 期末報告(Final report) 40%

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:Yes
[1] De Jong, Kenneth A. "Evolutionary computation: a unified approach." Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015. [2] Dumitrescu, Dumitru, et al. Evolutionary computation. CRC press, 2000.

The access to curricular consultation 1. Email: dcychiang@ntut.edu.tw

Expanding teaching and resources

The course corresponds to the SDGs 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 ● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）

Note
　

Instructor	Cheng-Yen Chiang　Office Hours
Email	dcychiang@ntut.edu.tw
Last Updated	2025-12-19 23:17:55
Course Objective	自然界的進化行為一直人類賦予計算機智慧學習的對象，但仿效自然界中生物間的適應性過程，建構出的龐大模型，一直是在一項艱鉅的挑戰，但不諱言的他的回報也是巨大的。進化計算便是基於生物遺傳學、自然選擇和出現的適應性行為等進化機制，建立出來的智慧型模型，以利智慧系統的建立。本課程將介紹各項演化計算方式，並以實作的方式實際了解彼此間的差異。 The evolutionary behavior of nature has always been the object of learning that man has endowed computer intelligence with. However, the construction of huge models that emulate the adaptive processes among organisms in nature has been a difficult challenge, but the rewards have been enormous. Evolutionary computation is an intelligent model based on evolutionary mechanisms such as biological genetics, natural selection, and emergent adaptive behaviors to facilitate the establishment of intelligent systems. This course will introduce the various evolutionary computation methods and provide a practical understanding of the differences between them in a hands-on manner.
Course Schedule	1. Introduction of evolutionary algorithm 2. Simple Genetic Algorithm 3. Evolution Strategy 4. Evolutionary Programming 5. Genetic Programming 6. Direction based Search 7. Implementation Encoding and Operators 8. Selection Methods 9. Replacement and Stop Criteria 10. Parameter Control 11. Performance Evaluation 12. Ant Colony Optimization 13. Particle Swarm Optimization 14. Artificial Immune Systems
Evaluation and grading policy	作業(Assignment and Report) 60% 期末報告(Final report) 40%
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:Yes [1] De Jong, Kenneth A. "Evolutionary computation: a unified approach." Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. 2015. [2] Dumitrescu, Dumitru, et al. Evolutionary computation. CRC press, 2000.
The access to curricular consultation	1. Email: dcychiang@ntut.edu.tw
Expanding teaching and resources
The course corresponds to the SDGs	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	● 深入探討AI的原理與應用（Explore the principles and applications of AI in depth）
Note