Evolutionary computation is concerned with the use of simulated biological evolution to solve problems for which it can be difficult to write the programs using traditional methods. This course examines different models of evolutionary computation and the kinds of problems to which they can be applied
Prerequisite(s)
-
Corequisite(s)
-
Special Requisite(s)
-
Instructor(s)
Course Assistant(s)
-
Schedule
The course is not opened for this semester.
Office Hour(s)
The course is not opened for this semester.
Teaching Methods and Techniques
Presentation, laboratory
Principle Sources
- Introduction to Evolutionary Computing, by Agoston E. Eiben (Author), J.E. Smith (Author), Natural Computing Series, Springer, 2010
Other Sources
- Hoos, H. H., & Stützle, T. (2004). Stochastic local search: Foundations and applications. Elsevier.
Course Schedules
Week
Contents
Learning Methods
1. Week
Introduction to course, Introduction to search and optimization, Local search techniques
Oral and written presentation.
2. Week
Local search techniques
Oral and written presentation.
3. Week
Local search techniques
Oral and written presentation.
4. Week
Evolutionary algorithms
Oral and written presentation.
5. Week
Evolutionary algorithms
Oral and written presentation.
6. Week
Evolutionary algorithms
Oral and written presentation.
7. Week
Introduction to swarm intelligence, Ant colony optimization
Oral and written presentation.
8. Week
Midterm exam
Midterm exam
9. Week
Particle swarm optimization, Artificial immune systems
Oral and written presentation.
10. Week
Working with nature inspired heuristics
Oral and written presentation.
11. Week
Other nature inspired heuristics
Oral and written presentation.
12. Week
Other nature inspired heuristics
Oral and written presentation.
13. Week
Constraint handling, Hybridization
Oral and written presentation.
14. Week
Advanced topics
Oral and written presentation.
15. Week
16. Week
17. Week
Assessments
Evaluation tools
Quantity
Weight(%)
Midterm(s)
1
40
Homework / Term Projects / Presentations
1
20
Final Exam
1
40
Program Outcomes
PO-1
Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied information in these areas to model and solve engineering problems.
PO-2
Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
PO-3
Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way so as to meet the desired result; ability to apply modern design methods for this purpose. (Realistic constraints and conditions may include factors such as economic and environmental issues, sustainability, manufacturability, ethics, health, safety issues, and social and political issues according to the nature of the design.)
PO-4
Ability to devise, select, and use modern techniques and tools needed for engineering practice; ability to employ information technologies effectively.
PO-5
Ability to design and conduct experiments, gather data, analyse and interpret results for investigating engineering problems.
PO-6
Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7
Ability to communicate effectively, both orally and in writing; knowledge of a minimum of one foreign language.
PO-8
Recognition of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9
Awareness of professional and ethical responsibility.
PO-10
Information about business life practices such as project management, risk management, and change management; awareness of entrepreneurship, innovation, and sustainable development.
PO-11
Knowledge about contemporary issues and the global and societal effects of engineering practices on health, environment, and safety; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1
Demonstrate an understanding of the basic principles and techniques of genetic algorithms, genetic programming and swarm intelligence.
LO-2
Demonstrate an understanding of how to apply techniques of genetic algorithms, genetic programming and swarm intelligence to optimisation problems and problems that require machine learning.
