K6717020 Social System Engineering

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Engineering

Lecture Code K6717020 Subject Classification Specialized Education

Subject Name 社会システム工学

Subject Name
（Katakana）シャカイシステムコウガク

Subject Name in
English Social System Engineering

Instructor HAYASHIDA TOMOHIRO

Instructor
(Katakana) ハヤシダ　トモヒロ

Campus Higashi-Hiroshima Semester/Term 3rd-Year,  First Semester,  1Term

Days, Periods, and Classrooms (1T) Mon7-8：ENG 103, (1T) Fri7-8：ENG 106

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (on-demand)

Lecture.
The procedure of this lecture (Face-to-Face / online) will be decided based on Hiroshima University's policy. The procedure will be announced using the bulletin board of Momiji to the registered students.

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 3 : Undergraduate High-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 11 : Electrical, Systems, and Control Engineering

Eligible Students Cluster 2 (Electrical, Computer and Systems Engineering), 3rd year students

Keywords Social system, Optimization, Genetic algorithms, Neural networks, Python
SDG_07, SDG_08, SDG_09, SDG_11, SDG_12

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) This class is a special subject for the cluster 2 students, and corresponds to "C: acquisition of expertise in electric, electronic, system and information fields and the ability to apply them" of the learning and educational goals. Please refer to related URL 2 for details of learning and educational goals of electrical, electronic, system, information system programs of cluster 2.
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Program of Electrical,Systems and Information Engineering
（Abilities and Skills）
・Concepts, knowledge and methods which are the basis for studies related to electrical,  systems, and information engineering.

Class Objectives
/Class Outline This course aims to develop an understanding of the foundations and applications of computational approaches—specifically optimization and machine learning—for solving problems in complex social systems such as transportation, logistics, and energy.
From the perspective of a system designer, students will examine how metaheuristics (Genetic Algorithms: GA) and machine learning (Neural Networks: NN) function under the uncertainty and complexity inherent in real-world social systems, and how these methods should be integrated. The course also cultivates the ability to articulate social problems in a form that can be addressed through engineering methodologies.
To understand engineering approaches to social systems, lectures will cover both the fundamentals and applications of optimization and machine learning. After organizing the characteristics of social systems and clarifying the limitations of classical mathematical programming methods, the course explores the roles of GA (Genetic Algorithms) and NN (Neural Networks), as well as strategies for integrating them to overcome those limitations.
Furthermore, to deepen understanding of the fundamental principles, students will engage in implementation exercises using Python. Through these activities, students will also develop a practical understanding of the “designer’s perspective” and key issues related to real-world social implementation.

Class Schedule Lecture 1
Guidance and Overview of Social Systems
Lecture 2
Introduction to Python
Lecture 3
Swarm Intelligence Algorithms
Lecture 4
Implementation of Particle Swarm Optimization Using Python
Lecture 5
Programming Exercise
Lecture 6
Overview of Genetic Algorithms
Lecture 7
Fundamentals of Genetic Algorithms
Lecture 8
Applications of Genetic Algorithms
Lecture 9
Midterm Examination
Lecture 10
Implementation of Genetic Algorithms (1)
Lecture 11
Implementation of Genetic Algorithms (2)
Lecture 12
Overview of Neural Networks
Lecture 13
Learning of Neural Networks (1)
Lecture 14
Learning of Neural Networks (2)
Lecture 15
Applications of Neural Networks

Students are required to submit some reports and take the intermediate and the final examinations

Text/Reference
Books,etc. Introduced in each lesson.

PC or AV used in
Class,etc. Handouts, Audio Materials, Visual Materials, Microsoft Teams, Microsoft Stream, moodle

(More Details) Distribute corresponding documents in each class.

Learning techniques to be incorporated Quizzes/ Quiz format, Post-class Report

Suggestions on
Preparation and
Review Please review about lecture contents beforehand and review contents which deepened your understanding through lecture content and subject exercises during lecture time.

Requirements

Grading Method The degree of achievement of the class goal is evaluated by midterm examination and final examination. The grade evaluation is decided by comprehensive evaluation which adds the daily learning attitude to the achievement degree of the class goal, and it passes it by 60% or more. The distribution shall be an intermediate exam (45%) final exam (45%), a practice question / report (10%).

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	School of Engineering
Lecture Code	K6717020	Subject Classification	Specialized Education
Subject Name	社会システム工学
Subject Name （Katakana）	シャカイシステムコウガク
Subject Name in English	Social System Engineering
Instructor	HAYASHIDA TOMOHIRO
Instructor (Katakana)	ハヤシダ　トモヒロ
Campus	Higashi-Hiroshima	Semester/Term	3rd-Year, First Semester, 1Term
Days, Periods, and Classrooms	(1T) Mon7-8：ENG 103, (1T) Fri7-8：ENG 106
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (on-demand)
Lecture. The procedure of this lecture (Face-to-Face / online) will be decided based on Hiroshima University's policy. The procedure will be announced using the bulletin board of Momiji to the registered students.
Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	3 : Undergraduate High-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	11 : Electrical, Systems, and Control Engineering
Eligible Students	Cluster 2 (Electrical, Computer and Systems Engineering), 3rd year students
Keywords	Social system, Optimization, Genetic algorithms, Neural networks, Python SDG_07, SDG_08, SDG_09, SDG_11, SDG_12
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	This class is a special subject for the cluster 2 students, and corresponds to "C: acquisition of expertise in electric, electronic, system and information fields and the ability to apply them" of the learning and educational goals. Please refer to related URL 2 for details of learning and educational goals of electrical, electronic, system, information system programs of cluster 2.
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Program of Electrical,Systems and Information Engineering （Abilities and Skills）・Concepts, knowledge and methods which are the basis for studies related to electrical, systems, and information engineering.
Class Objectives /Class Outline	This course aims to develop an understanding of the foundations and applications of computational approaches—specifically optimization and machine learning—for solving problems in complex social systems such as transportation, logistics, and energy. From the perspective of a system designer, students will examine how metaheuristics (Genetic Algorithms: GA) and machine learning (Neural Networks: NN) function under the uncertainty and complexity inherent in real-world social systems, and how these methods should be integrated. The course also cultivates the ability to articulate social problems in a form that can be addressed through engineering methodologies. To understand engineering approaches to social systems, lectures will cover both the fundamentals and applications of optimization and machine learning. After organizing the characteristics of social systems and clarifying the limitations of classical mathematical programming methods, the course explores the roles of GA (Genetic Algorithms) and NN (Neural Networks), as well as strategies for integrating them to overcome those limitations. Furthermore, to deepen understanding of the fundamental principles, students will engage in implementation exercises using Python. Through these activities, students will also develop a practical understanding of the “designer’s perspective” and key issues related to real-world social implementation.
Class Schedule	Lecture 1 Guidance and Overview of Social Systems Lecture 2 Introduction to Python Lecture 3 Swarm Intelligence Algorithms Lecture 4 Implementation of Particle Swarm Optimization Using Python Lecture 5 Programming Exercise Lecture 6 Overview of Genetic Algorithms Lecture 7 Fundamentals of Genetic Algorithms Lecture 8 Applications of Genetic Algorithms Lecture 9 Midterm Examination Lecture 10 Implementation of Genetic Algorithms (1) Lecture 11 Implementation of Genetic Algorithms (2) Lecture 12 Overview of Neural Networks Lecture 13 Learning of Neural Networks (1) Lecture 14 Learning of Neural Networks (2) Lecture 15 Applications of Neural Networks Students are required to submit some reports and take the intermediate and the final examinations
Text/Reference Books,etc.	Introduced in each lesson.
PC or AV used in Class,etc.	Handouts, Audio Materials, Visual Materials, Microsoft Teams, Microsoft Stream, moodle
(More Details)	Distribute corresponding documents in each class.
Learning techniques to be incorporated	Quizzes/ Quiz format, Post-class Report
Suggestions on Preparation and Review	Please review about lecture contents beforehand and review contents which deepened your understanding through lecture content and subject exercises during lecture time.
Requirements
Grading Method	The degree of achievement of the class goal is evaluated by midterm examination and final examination. The grade evaluation is decided by comprehensive evaluation which adds the daily learning attitude to the achievement degree of the class goal, and it passes it by 60% or more. The distribution shall be an intermediate exam (45%) final exam (45%), a practice question / report (10%).
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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