• Sep. 30 - Lecture notes #1

• Course Description - TBA
• Course Objectives - TBA
• Evaluation - TBA
  • 1st Presentation
  • 2nd Presentation
  • Final Presentation
• Lab. Experiments - TBA
• Lab. Experiments Tools - TBA

• Instructor - Dae-ki Kang, Ph.D.

• Lectures : Thursday 3pm-6pm @ U308
• Office Hours : by appointment thru e-mail, or Monday 10am-5pm

• Primary Text - Essentials of Metaheuristics, Sean Luke
• Additional References - 쉽게 배우는 유전 알고리즘/문병로/한빛미디어
• Lecture Notes - Week 1
• Quizes, Homeworks, Tests - TBA

Course Materials

• Week 1
• Set of paper for presentation
  1. Monkey Search - Monkey Search: A Novel Meta-Heuristic Search for Global Optimization, A. Mucherino and O. Seref, Monkey Search: A Novel Meta-Heuristic Search for Global Optimization, Proceedings of the Conference “Data Mining, System Analysis and Optimization in Biomedicine”, Gainesville, Florida, March 2007, O. Seref, O.E. Kundakcioglu, P.M. Pardalos (Eds.), AIP Conference Proceedings 953, 162–173, 2007. Download paper. Show abstract.
  2. Cuckoo search - Cuckoo search via Levy flights, Yang, X.-S.; Deb, S. (2009). Cuckoo search via Levy flights, in: World Congress on Nature & Biologically Inspired Computing (NaBIC 2009).. IEEE Publication, USA. pp. 210–214.
  3. Firefly algorithm - Nature-Inspired Metaheuristic Algorithms, X. S. Yang, Firefly algorithms for multimodal optimization, in: Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, Vol. 5792, pp. 169-178 (2009). eprint at http://arxiv.org/abs/1003.1466
  4. Honey-bee mating optimization - Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization, Haddad, O. B. et al.; Afshar, Abbas; Mariño, Miguel A. (2006). "Honey-bees mating optimization (HBMO) algorithm: a new heuristic approach for water resources optimization". Water Resources Management 20: 661–680. doi:10.1007/s11269-005-9001-3
  5. Artificial Bee Colony Algorithm - An idea based on honey bee swarm for numerical optimization, D. Karaboga, AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,TECHNICAL REPORT-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005. (PDF)
  6. Glowworm swarm optimization - Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions, Krishnanand, K.; Ghose, D. (2009). "Glowworm swarm optimization for simultaneous capture of multiple local optima of multimodal functions". Swarm Intelligence 3: 87–124. doi:10.1007/s11721-008-0021-5.
  7. Bee colony optimization - On honey bees and dynamic allocation in an internet server colony, Nakrani, S.; Tovey, S. (2004). "On honey bees and dynamic server allocation in Internet hosting centers". Adaptive Behaviour 12.
  8. Non-dominated sorting genetic algorithm (NSGA-II) - A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. (2002). "A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II". IEEE Transactions on Evolutionary Computation 6 (2): 182–197. doi:10.1109/4235.996017.
  9. Harmony search - A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice, Slides, Geem, Z.W.; Kim, J.H.; Loganathan, G.V. (2001). "A new heuristic optimization algorithm: harmony search". Simulation 76.
  10. Cross entropy method - Optimization of Computer simulation Models with Rare Events, Rubinstein, R.Y. (1997). "Optimization of Computer simulation Models with Rare Events". European Journal of Operations Research 99: 89–112. doi:10.1016/S0377-2217(96)00385-2.
  11. Differential evolution
  12. CMA-ES
  13. estimation of distribution algorithm
  14. no free lunch theorems
  15. Particle swarm optimization
  16. NSGA
  17. MOGA
  18. Ant colony algorithm
  19. Interactive evolutionary computation
  20. Memetic algorithm
  21. metaplan for tuning an optimizer's parameters by using another optimizer
  22. artificial immune system
  23. Tabu search
  24. Simulated annealing
  25. metaplan for tuning an optimizer's parameters by using another optimizer
  26. adaptation of control parameters for an optimizer
  27. Metropolis-Hastings algorithm
  28. Simplex method
• Links
  • Intelligent Water Drops - The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, Hamed Shah-Hosseini, The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm, [1], International Journal of Bio-Inspired Computation, Vol. 1, Nos. 1/2, pp. 71-79, 2008.
  • Metaheuristic in Wikipedia
  • Multiobjective optimization
  • Evolutionary algorithm in Wikipedia
  • Evolutionary computation in Wikipedia