COURSE INFORMATION Course Title: METAHEURISTICS

Code	Course Type	Regular Semester	Theory	Practice	Lab	Credits	ECTS
CEN 814	A	2	3	0	0	3	7.5

Academic staff member responsible for the design of the course syllabus (name, surname, academic title/scientific degree, email address and signature)	NA
Lecturer (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	Ali Osman Topal
Second Lecturer(s) (name, surname, academic title/scientific degree, email address and signature) and Office Hours:	NA
Teaching Assistant(s) and Office Hours:	NA
Language:	English
Compulsory/Elective:	Elective
Classroom and Meeting Time:	On Friday 10:00 / 13:00
Course Description:	-
Course Objectives:	Meta-heuristic optimization algorithms are artificial intelligence search methods that can be used to find the optimal decisions for designing or managing a wide range of complex systems. This course describes a variety of (meta) heuristic search methods including simulated annealing, tabu search, genetic algorithms, genetic programming, dynamically dimensioned search, and multiobjective methods. Algorithms will be used to find values of discrete and/or continuous variables that optimize system performance or improve system reliability. Students can select application projects from a range of application areas. The advantages and disadvantages of heuristic search methods for both serial and parallel computation are discussed in comparison to other optimization algorithms.

COURSE OUTLINE

Week	Topics
1	Introduction and Overview of Heuristic and Meta-Heuristic Search
2	General optimization problems
3	Fitness functions
4	Local search vs. Meta-heuristic search
5	Visualization of the Search Landscape
6	Hill Climbing
7	Simulated Annealing
8	Tabu Search
9	Genetic Algorithms
10	Swarm Optimization: Particle Swarm Optimization
11	Swarm Optimization: Ant Colony Optimization
12	Differential Evolution
13	Multi-Objective Optimization
14	Review

Prerequisite(s):	Analysis of Algorithms
Textbook:	Zbigniew Michalewicz and David B. Fogel. 2004. How to Solve It: Modern Heuristics. Springer
Other References:	Fourer, Gay, and Kernighan. 2002. AMPL: A Modeling Language for Mathematical Programming. Cengage Learning, 2nd Edition
Laboratory Work:
Computer Usage:	MATLAB
Others:	No

COURSE LEARNING OUTCOMES

1	Gain an understanding (both from a theoretical perspective and through implementation) of the foundations of meta-heuristic search. Types of algorithms that will be discussed include hill-climbing, simulated annealing, genetic algorithms, genetic programming, ant-colony optimization and tabu search.
2	Understand how to model engineering problems in such a way that these techniques can be applied.
3	Learn how to read,understand and critique scientific research articles.
4	Complete a course long project on a research topic that uses AI to solve a engineering problem.

COURSE CONTRIBUTION TO... PROGRAM COMPETENCIES (Blank : no contribution, 1: least contribution ... 5: highest contribution)

No	Program Competencies	Cont.
Doctorate (PhD) in Computer Engineering Program
1	Engineering graduates with sufficient theoretical and practical background for a successful profession and with application skills of fundamental scientific knowledge in the engineering practice.	5
2	Engineering graduates with skills and professional background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations	5
3	Engineering graduates with the necessary technical, academic and practical knowledge and application confidence in the design and assessment of machines or mechanical systems or industrial processes with considerations of productivity, feasibility and environmental and social aspects.	5
4	Engineering graduates with the practice of selecting and using appropriate technical and engineering tools in engineering problems, and ability of effective usage of information science technologies.	5
5	Ability of designing and conducting experiments, conduction data acquisition and analysis and making conclusions.	5
6	Ability of identifying the potential resources for information or knowledge regarding a given engineering issue.	5
7	The abilities and performance to participate multi-disciplinary groups together with the effective oral and official communication skills and personal confidence.	4
8	Ability for effective oral and official communication skills in foreign language.	4
9	Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology.	5
10	Engineering graduates with well-structured responsibilities in profession and ethics.	4
11	Engineering graduates who are aware of the importance of safety and healthiness in the project management, workshop environment as well as related legal issues.	4
12	Consciousness for the results and effects of engineering solutions on the society and universe, awareness for the developmental considerations with contemporary problems of humanity.	4

COURSE EVALUATION METHOD

Method	Quantity	Percentage
Homework	4	10
Project	1	60
	Total Percent:	100%

ECTS (ALLOCATED BASED ON STUDENT WORKLOAD)

Activities	Quantity	Duration(Hours)	Total Workload(Hours)
Course Duration (Including the exam week: 16x Total course hours)	16	3	48
Hours for off-the-classroom study (Pre-study, practice)	16	3	48
Mid-terms			0
Assignments	4	12	48
Final examination			0
Other	1	43.5	43.5
Total Work Load:			187.5
Total Work Load/25(h):			7.5
ECTS Credit of the Course:			7.5