This course presents analytic concepts in soft computing techniques. It is designed for the beginning graduate students who are interesting in application of soft computing techniques to engineer problems. Students should have a mathematical maturity typical of undergraduate curricula in science and engineering, including calculus and discrete mathematics. The teaching approach in this course emphasizes modeling, analysis and design principles of soft computing techniques. The subject material includes genetic algorithm, Ant Colony Optimization and Particle Swarm Optimization. Case studies include several engineering applications.
1 Administrative/Introduction 2 Fundamental of Genetic Algorithm 3 Fundamental of Genetic Algorithm HW#1 4 Constrained Optimization Problem 5 HW#1 Presentation HW#1 Due 6 Constrained Optimization Problem HW#2 7 Combinatorial Optimization Problems 8 HW#2 Presentation HW#2 Due 9 Combinatorial Optimization Problems 10 Combinatorial Optimization Problems 11 Mid-Term Exam 12 Mid-Term Report Presentation 13 Flow-Shop Sequence Problems Final project proposal 14 Flow-Shop Sequence Problems 15 Job-Shop Scheduling Class Evaluation 16 ACO/PSO 17 Final Project Presentation 18 Final Exam Final Project Report Due
1. Home Work 30% 2. Midterm Report 20% 3. Midterm Exam 20% 4. Final Project/Report 20% 5. Participation/popup-quiz 10%
Mitsuo Gen and Runwei Cheng, Genetic Algorithms & Engineering Design, John Wiley & Sons, Inc., 1997. Selected technical papers.