Course Code & Number
IE 435
Course Title
Heuristic Search
Credit Hours/ ECTS Credits
(3+0+0) 3 TEDU Credits, 5 ECTS Credits
Mode of Delivery:
Face-to-face
Language of Instruction:
English
Pre-requisite / Co-requisite::
Pre-requisites: IE 232
Co-requisites: NONE
Catalog Description
Introduction and categorization of heuristics. Overview of basic heuristic search procedures. Metaheuristics. Simulated annealing. Tabu search. Genetic algorithms. Ant colony algorithms. Evaluation of heuristic performance. Computational complexity of heuristics.
Course Objectives
The course aims to teach approximation methods and metaheuristic algorithms to solve combinatorial problems. The methods include basic search procedures, simulated annealing, tabu search, genetic algorithms ant colony algorithms and their hybrids. Ultimate goal is to teach techniques for designing a heuristic algorithm for an engineering problem, evaluatingits performance and computational complexity.
Course Learning Outcomes
Upon succesful completion of this course, a student will be able to
1. Comprehend the basic types of heuristic methods(B2, a2)
2. Apply fundamental concepts of heuristics in solving various optimization problems [B3] [a2]
3. Use metaheuristics including simulated annealing, tabu search, genetic algorithms, ant algorithms and their hybrids. [B3] [a2]
4. Compare the quality of different heuristic approaches (B6, b2,c)
5. Solve an engineering problem by using an appropriate heuristic method . [e, k] [B3]
6. Analyse the results of a heuristic method for an engineering problem [b2,] [B4]
Learning Activities and Teaching Methods:
Telling/Explaining
Discussion/Debate
Reading
Case Study/Scenarion Analysis
Simulation & Games
Assessment Methods and Criteria:
Test / Exam
Quiz
Case Studies / Homework
Assessment Methods and Criteria Others:
Recommended Reading
1. Reeves (1993). In Modern Heuristic Techniques for Combinatorial Problems (edited by Reeves), Wiley
2. Pearl (1984). Heuristics: Intelligent Search Strategies for Computer Problem Solving, Addison-Wesley
3. Laguna M (2002). In Handbook of Applied Optimization (edited by Pardalos PM and Resende MGC), Oxford Academic Press
4. Rayward-Smith V.J., Osman I.H. Reeves CR and Smith GD (eds.) (1996), Modern Heuristic Search Methods, Wiley
Required Reading
Michalewicz Z. and Fogel D. B. (2000), How to Solve It: Modern Heuristics, Springer
Learning Activities and Teaching Methods Others:
Course & Program Learning Outcome Matching:
