萬用啟發式演算法

This course gives a broad introduction to metaheuristic algorithms, including Evolutionary Computation, Simulated Annealing (SA), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), Fuzzy Set, Artificial Immune System (AIS), and so on. Case studies for a variety of problems arising from management science will also be discussed.

Computer programming, operations research, and basic probability theory.

All the materials can be downloaded from http://e3.nctu.edu.tw/

Participation (5%) Homework (25%) Midterm paper presentation (25%) Term project (45%)

• Management Science
• Metaheuristic Algorithm

The lecture is given based on handouts. Parts of the handouts are referred to the following books and articles: 1. M. Gendreau and J.-Y. Potvin, Handbook of Metaheuristics, 2nd Edition, Springer, 2010. 2. H. H. Hoos and T. Stutzle, Stochastic Local Search : Foundations and Applications, 1st edition, Morgan Kufmann/Elsevier, 2004. 3. E. K. Burke and G. Kendall, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, Springer, 2005. 4. X. Yang, Engineering Optimization: An Introduction with Metaheuristic Applications, 2010. 5. A. Konar, Computational Intelligence: Principles, Techniques and Applications, Springer, 2005. 6. B. W. Taylor, Introduction to Management Science, 10th edition, 2009. 7. T. Gupta and B. K. Ghosh, "A survey of expert systems in manufacturing and process planning," Computers in Industry, 11(2): 195-204.

地點

MB 501

時間

Tue. 12:00-13:00 (e-mail contact in advance)

聯絡方式

cclin321@nctu.edu.tw