TÜRKÇE
ENGLISH
Information
| Code | CENG548 |
| Name | Nature-Inspired Computing |
| Term | 2023-2024 Academic Year |
| Term | Spring |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Yüksek Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MUSTAFA ORAL |
| Course Instructor |
1 |
Course Goal / Objective
The primary objective of this course is to examine nature-inspired computational methods in artificial life, evolutionary computing, and related fields, with an emphasis on understanding the basic computational principles involved.
Course Content
Conceptual Framework definitions, terminology, introduction to different paradigms, core concepts such as self- organization and emergence, history, overview .Cellular Automata: basics, properties, environments, self-replicating machines, adaptation, applications. Multi-Agent Artificial Life Worlds: flocking, swarm intelligence, ant colony optimization;Neural Nets:Genetic Algorithms: biology, method, variants, applications;Genetic Algorithms: biology, method, variants, applications; Evolution Strategies: method, variations, optimization;Advanced/Research Topics in Nature-Inspired Computation:
Course Precondition
None
Resources
Nabiyev V. V., 2005 Yapay Zeka: Problemler, Yöntemler, Algoritmalar, Ankara (2. Baskı) 2 Russell, Stuart J. ; Norvig, Peter, 2003 , Artificial Intelligence: A Modern Approach (2nd ed. )
Notes
Nilsson, Nils,1998 , Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, ISBN 978-1-55860-467-4
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Describe the natural phenomena that motivate the discussed algorithms. |
| LO02 | Understand the strengths, weaknesses and appropriateness of nature-inspired algorithms. |
| LO03 | Apply nature-inspired algorithms to optimization, design and learning problems. |
| LO04 | Understand fundamental concepts of NP-hardness and computational complexity. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. | 3 |
| PLO02 | Bilgi - Kuramsal, Olgusal | By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. | 5 |
| PLO03 | Yetkinlikler - Öğrenme Yetkinliği | Being aware of the new and developing practices of his / her profession and examining and learning when necessary. | 4 |
| PLO04 | Yetkinlikler - Öğrenme Yetkinliği | Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. | 5 |
| PLO05 | Yetkinlikler - Öğrenme Yetkinliği | Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process. | |
| PLO06 | Yetkinlikler - Öğrenme Yetkinliği | Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Has the skills of learning. | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. | 4 |
| PLO09 | Beceriler - Bilişsel, Uygulamalı | Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. | 2 |
| PLO10 | Beceriler - Bilişsel, Uygulamalı | Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. | 3 |
| PLO11 | Beceriler - Bilişsel, Uygulamalı | Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. | 1 |
| PLO12 | Bilgi - Kuramsal, Olgusal | Observes social, scientific and ethical values in all professional activities. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Conceptual Framework definitions, terminology, introduction to different paradigms, core concepts such as self- organization and emergence, history, overview | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 2 | Cellular Automata: basics, properties, environments, self-replicating machines, adaptation, applications | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 3 | Multi-Agent Artificial Life Worlds: flocking, swarm intelligence, ant colony optimization | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 4 | Neural Nets: | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 5 | Genetic Algorithms 1: biology, method, variants, applications | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 6 | Genetic Algorithms 2 : biology, method, variants, applications | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 7 | Evolution Strategies: method, variations, optimization | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | co-evolution, speciation, creative evolutionary systems, network representations and genetic operations, spatially-distributed populations | Reading Course materials | Öğretim Yöntemleri: Anlatım |
| 10 | Evolving Neural Networks | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 11 | Advanced/Research Topics in Nature-Inspired Computation: Forest Algorithm | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 12 | Advanced/Research Topics in Nature-Inspired Computation: Bacterial foraging optimization Algorithm (BFOA) | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 13 | Advanced/Research Topics in Nature-Inspired Computation: Firefly Algorithm (FFA) | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 14 | Advanced/Research Topics in Nature-Inspired Computation:Flower Pollination Algorithm (FPA) | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 15 | Advanced/Research Topics in Nature-Inspired Computation:Cuckoo Search Algorithm (CSA) | Reading Course materials | Öğretim Yöntemleri: Anlatım, Beyin Fırtınası |
| 16 | Term Exams | Preparation for project presentation | Ölçme Yöntemleri: Yazılı Sınav, Ödev, Proje / Tasarım, Performans Değerlendirmesi |
| 17 | Term Exams | Preparation for project presentation | Ölçme Yöntemleri: Ödev, Proje / Tasarım, Performans Değerlendirmesi |
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 | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||