Information Package / Course Catalogue
Introduction to Metaheuristics
Course Code: CSE438
Course Type: Area Elective
Couse Group: First Cycle (Bachelor's Degree)
Education Language: English
Work Placement: N/A
Theory: 2
Prt.: 2
Credit: 3
Lab: 0
ECTS: 6
Objectives of the Course

Introduce some important optimization problems. Introduce some conventional optimization methods. Introduce single-point (local search) methods, including tabu search (TS), simulated annealing (SA), iterated local search (ILS), and guided local search (GLS). Introduce multi-point (global search) methods, such as genetic algorithm (GA), memetic algorithm (MA), and artificial bee colony (ABC).

Course Content

This course introduces the students to the fundamental concepts of metaheuristic optimization.

Name of Lecturer(s)
Lec. Gözde ALP
Learning Outcomes
1.Understand the basic ideas of the introduced metaheuristic algorithms
2.Know how to use metaheuristics for solving practical problems
3.Become more interested in developing new metaheuristics for solving different types of problems
4.Ability to adapt heuristics to problems especially in Computer Engineering
5.Developing heuristic methods adapted to problems
Recommended or Required Reading
1.Talbi, E. G. (2009). Metaheuristics: from design to implementation (Vol. 74). John Wiley & Sons.
Weekly Detailed Course Contents
Week 1 - Theoretical & Practice
Classification of optimization problems and case studies
Week 2 - Theoretical & Practice
A brief review of conventional search algorithms
Week 3 - Theoretical & Practice
Simulated annealing
Week 4 - Theoretical & Practice
Iterated local search and guided local search
Week 5 - Theoretical & Practice
Tabu search
Week 6 - Theoretical & Practice
Team work I: solving problems using single-point search algorithms
Week 7 - Theoretical & Practice
Team work I: Presentation of team-work results
Week 8 - Theoretical & Practice
Team work I: Presentation of team-work results
Week 9 - Theoretical & Practice
Genetic algorithm
Week 10 - Theoretical & Practice
Genetic algorithm
Week 11 - Theoretical & Practice
Memetic algorithm
Week 12 - Theoretical & Practice
Artificial bee colony
Week 13 - Theoretical & Practice
Team work II: solving problems using multi-point search algorithms
Week 14 - Theoretical & Practice
Team work II: Presentation of team-work results
Assessment Methods and Criteria
Type of AssessmentCountPercent
Final Examination1%50
Assignment1%10
Term Assignment2%40
Workload Calculation
ActivitiesCountPreparationTimeTotal Work Load (hours)
Lecture - Theory140228
Lecture - Practice140228
Assignment114014
Term Project218036
Individual Work130226
Final Examination116218
TOTAL WORKLOAD (hours)150
Contribution of Learning Outcomes to Programme Outcomes
PÇ-1
PÇ-2
PÇ-3
PÇ-4
PÇ-5
PÇ-6
PÇ-7
PÇ-8
PÇ-9
PÇ-10
PÇ-11
OÇ-1
4
4
3
4
5
2
2
4
2
2
5
OÇ-2
4
5
4
5
4
2
2
4
2
2
5
OÇ-3
3
4
4
4
5
2
2
4
3
3
5
OÇ-4
4
5
5
5
5
3
2
4
3
3
5
OÇ-5
4
5
5
5
5
3
2
4
3
3
5
Adnan Menderes University - Information Package / Course Catalogue
2026