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Information
| Unit | INSTITUTE OF NATURAL AND APPLIED SCIENCES |
| ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING (MASTER) (WITH THESIS) | |
| Code | YZ006 |
| Name | Nature Inspired Computing and Optimization |
| Term | 2025-2026 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | Türkçe |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | |
| Course Instructor |
The current term course schedule has not been prepared yet. Previous term groups and teaching staff are shown.
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Course Goal / Objective
Students will be familiar with nature-inspired computing methods, which are fast solution methods to optimization problems, and gain the ability to use them
Course Content
Nature-inspired computational algorithms, mathematical modeling, adaptation and programming of fast solution approaches for mathematical models
Course Precondition
Intermediate knowledge of Python programming language
Resources
Fouad Bennis, Rajib Kumar Bhattacharjya, Nature-Inspired Methods for Metaheuristics Optimization, Springer, 2020, 978-3-030-26457-4
Notes
Lecture slides
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Learn the basic concepts and principles of nature-inspired computing and optimization. |
| LO02 | Understand how common nature-inspired algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial bee colony algorithm work. |
| LO03 | Apply these algorithms to specific problems and analyze the results. |
| LO04 | Understand the mathematical and theoretical foundations of nature inspired algorithms. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Beceriler - Bilişsel, Uygulamalı | To be able to access information broadly and deeply by conducting scientific research in the field, to be able to evaluate, interpret and apply the information. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Has a comprehensive knowledge of current techniques and methods applied in engineering and their limitations. | 4 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | To be able to use uncertain, limited or incomplete data to complete and apply knowledge using scientific methods; to be able to use knowledge from different disciplines together. | 4 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Is aware of new and emerging practices of the profession, examines and learns them when needed. | 5 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Defines and formulates problems related to the field, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Develops new and/or original ideas and methods; designs complex systems or processes and develops innovative/alternative solutions in their designs. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Designs and implements theoretical, experimental and modeling-based research; examines and solves complex problems encountered in this process. | |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | To be able to work effectively in disciplinary and multidisciplinary teams, to lead such teams and to develop solution approaches in complex situations; to be able to work independently and take responsibility. | 4 |
| PLO09 | Bilgi - Kuramsal, Olgusal | To be able to communicate orally and in writing in a foreign language at least at the B2 level of the European Language Portfolio. | |
| PLO10 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | To be able to communicate the process and results of his/her studies systematically and clearly in written or oral form in national and international environments in or outside the field. | |
| PLO11 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Knows the social, environmental, health, safety, legal, project management and business life practices of engineering applications and is aware of the constraints these impose on engineering applications. | |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in the stages of data collection, interpretation and announcement and in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction and Basic Concepts | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 2 | Mathematical Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 3 | Overview of Population-Based Methods | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 4 | Genetic Algorithm | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 5 | Particle Swarm Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 6 | Ant Colony Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 7 | Artificial Bee Colony Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Overview of Individual-Based Methods | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 10 | Simulated Annealing Algorithm | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 11 | Other Nature Inspired Methods | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 12 | Hyper Parameter Optimization | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 13 | Software and Tools | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 14 | Application and Project Presentations - 1 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 15 | Application and Project Presentations - 2 | Preliminary research on the subject | Öğretim Yöntemleri: Anlatım |
| 16 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Preparation for the exam | Ölçme Yöntemleri: Yazılı Sınav |
Assessment (Exam) Methods and Criteria
Current term shares have not yet been determined. Shares of the previous term are shown.
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 50 | 20 |
| 1. Homework | 50 | 20 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 1 | 15 | 15 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 20 | 20 |
| Total Workload (Hour) | 162 | ||
| Total Workload / 25 (h) | 6,48 | ||
| ECTS | 6 ECTS | ||