Türkçe | English
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING
Computer Engineering, Masters with Thesis
Course Catalog
http://ceng.ktu.edu.tr
Phone: +90 0462 3773157
FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of COMPUTER ENGINEERING / Computer Engineering, Masters with Thesis
Katalog Ana Sayfa
  Katalog Ana Sayfa  KTÜ Ana Sayfa   Katalog Ana Sayfa
 
 

BILL5170Smart Optimization Algorithms3+0+0ECTS:7.5
Year / SemesterSpring Semester
Level of CourseSecond Cycle
Status Elective
DepartmentDEPARTMENT of COMPUTER ENGINEERING
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 3 hours of lectures per week
LecturerDr. Öğr. Üyesi Hülya DOĞAN
Co-Lecturer
Language of instructionTurkish
Professional practise ( internship ) None
 
The aim of the course:
The course intends to teach the students for computational problem solving techniques from the fields of computational Intelligence, Biologically Inspired Computation, and Metaheuristics Optimization.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Understand what optimization.1,2,3,41,3
PO - 2 : Gain knowledge on Artificial Intelligence and Algorithms.1,2,3,4,131,3
PO - 3 : Compare various problem solving algorithms and technique-specific guidelines.1,2,5,131,3
PO - 4 : Implement state-of-the-art algorithms to address business or scientific needs.2,3,4,5,11,121,3
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
Introduction to Intelligence Algorithm and Metaheuristic Optimization, Stochastic Algorithms, Evolutionary Algorithms, Physical Algoritmhs, Probabilistic Algorithms, Swarm Optimization Algorithms, Immune Algorithms, Programming Paradigms, Testing Algorithms, Problem Solving Strategies
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Introduction to Intelligence Algorithm and Metaheuristic Optimization
 Week 2Stochastic Algorithms : Random Search, Stochastic Hill Climbing
 Week 3Stochastic Algorithms : Local Search
 Week 4Stochastic Algorithms : Tabu Search
 Week 5Evolutionary Algorithms: Genetic Algorithm, Genetic Programming,
 Week 6Evolutionary Algorithms: Differential Evolution, Evolutinary programming
 Week 7Evolutionary Algorithms: Learning Classifier System,
 Week 8Physical Algoritmhs: Simulated Annealing, Cultural Algorithm,
 Week 9Mid-term exam
 Week 10Probabilistic Algorithms: Population-Based Incremental Learning,
 Week 11Probabilistic Algorithms: Bayesian Optimization Algorithm, Cross-Entropy Method
 Week 12Swarm Optimization Algorithms: Particle Swarm Optimization, Ant Colony System, Bees Algorithm
 Week 13Swarm Optimization Algorithms: Optimization Algorithm, Bat Algorithm
 Week 14Immune Algorithms: Selection Algorithms, Artificial Immune Recognition System,
 Week 15Programming Paradigms, Testing Algorithms, Problem Solving Strategies
 Week 16Final Exam
 
Textbook / Material
1Clever Algorithms: Nature-Inspired Programming Recipes, Jason Brownlee, Creative Commons, 2011(438 sayfa), ISBN: 978-1-4467-8506-5
2Swarm Intelligence and Bio-Inspired Computation Theory and Applications, Xin-She Yang, Zhihua Cui, Renbin Xiao, Amir Hossein Gandomi, and Mehmet Karamanoglu, Elsevier, First edition 2013(420 sayfa)
3Theory and New Applications of Swarm Intelligence, Edited by Rafael Parpinelli and Heitor S. Lopes, InTech, 2012(204 sayfa), ISBN 978-953-51-0364-6
4Yapay Zeka Optimizasyon Algoritmaları, Derviş Karaboğa, Nobel Yayın Dağıtım, 2004 (199 sayfa).
 
Recommended Reading
1Nature-inspired Metaheuristic Algorithms, Xin-She Yang, Luniver Press, 2010 (160 sayfa).
2Engineering Optimization: An Introduction with Metaheuristic Applications, Xin-She Yang, Wiley Press, 2010 (347 pages).
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 2 20
Presentation 14 1 15
Homework/Assignment/Term-paper 13 1 25
End-of-term exam 16 2 40
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 7 14 98
Laboratuar çalışması 0 0 0
Arasınav için hazırlık 6 2 12
Arasınav 2 1 2
Uygulama 0 0 0
Klinik Uygulama 0 0 0
Ödev 3 10 30
Proje 3 9 27
Kısa sınav 0 0 0
Dönem sonu sınavı için hazırlık 6 2 12
Dönem sonu sınavı 2 1 2
Diğer 1 0 0 0
Diğer 2 0 0 0
Total work load225