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Description of Individual Course Units| Course Unit Code | Course Unit Title | Type of Course Unit | Year of Study | Semester | Number of ECTS Credits | | EM2112 | Heuristic Optimization | Elective | 2 | 3 | 4 |
| | Level of Course Unit | | First Cycle | | Objectives of the Course | | Upon successful completion of this course students, intuitive method of how and why it works, when it should be used, and the advantages of traditional approaches to mathematical programming | | Name of Lecturer(s) | | Doç.Dr. Erdemalp Özden | | Learning Outcomes | | 1 | Student, simulated annealing, genetic algorithms, evolutionary strategies and TABU search methods commonly used such as variety of intuitive will obtain information about. | | 2 | Students, using intuitive methods of analysis and common model is simple | | 3 | Student, neural networks and computerised methods, such as will some other heuristic methods you've learned. | | 4 | Students use the results obtained by using the intuitive yöntemeri yorumlayabilecektir. |
| | Mode of Delivery | | Normal Education | | Prerequisites and co-requisities | | None | | Recommended Optional Programme Components | | None | | Course Contents | | Combinatorial various intuitive techniques for solving problems. The intuitive reason for existence of techniques, skills and uygulanabilirlikleri. | | Weekly Detailed Course Contents | |
| 1 | Introduction: the complexity and algorithmic calculation growth speed, combinatorial problems
| - | - | | 2 | Branch-bound method: branching, restrictions, nod development
| - | - | | 3 | Dominance, relieving to provide border, integer programming
| - | - | | 4 | Lagrange relieving method
| - | - | | 5 | Local research: neighborhoods, local and global goodness, constructive and healing intuitive techniques
| - | - | | 6 | Local research: neighborhoods, local and global goodness, constructive and healing intuitive techniques
| - | - | | 7 | Simulated annealing, general approach, and cool-down charts
| - | - | | 8 | Midterm Exam
| - | - | | 9 | Genetic algorithms: populations, reproduction, crossover
| - | - | | 10 | Genetic algorithms: populations, reproduction, crossover
| - | - | | 11 | Mutation, textures, competition and genetic programming
| - | - | | 12 | TABU search: short-term memory, TABOO status, targeting, reinforcing and diversification
| - | - | | 13 | Other methods and techniques: neural networks, random methods, hybrid methods
| - | - | | 14 | Deluge algorithm, from record transfer and parallel application
| - | - | | 15 | Deluge algorithm, from record transfer and parallel application 2 | | |
| | Recommended or Required Reading | | Reeves, c. r., Modern Heuristic Techniques for Combinatorial Problems, John Wiley & Sons, 1993.
SAIT, a hog, and Youssef, h., Iterative Algorithms with Applications in Engineering, IEEE Press, 1999. | | Planned Learning Activities and Teaching Methods | | | Assessment Methods and Criteria | |
| Midterm Examination | 1 | 100 | | SUM | 100 | |
| Final Examination | 1 | 100 | | SUM | 100 | | Term (or Year) Learning Activities | 40 | | End Of Term (or Year) Learning Activities | 60 | | SUM | 100 |
| | Language of Instruction | | Turkish | | Work Placement(s) | | None |
| | Workload Calculation | |
| Midterm Examination | 1 | 1 | 1 | | Final Examination | 1 | 2 | 2 | | Attending Lectures | 14 | 3 | 42 | | Individual Study for Mid term Examination | 5 | 4 | 20 | | Individual Study for Final Examination | 10 | 3 | 30 | | Homework | 4 | 3 | 12 | |
| Contribution of Learning Outcomes to Programme Outcomes | | LO1 | 4 | | | 5 | 5 | | 3 | 4 | | LO2 | | | 3 | 4 | | | 3 | 3 | | LO3 | 4 | 4 | | | | | 4 | 2 | | LO4 | | | 5 | | | | 3 | 3 |
| | * Contribution Level : 1 Very low 2 Low 3 Medium 4 High 5 Very High |
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