The principle of optimization will be explained in detail. The working principles of some traditional tools of optimization, namely exhaustive search method, random walk method, steepest descent method will be discussed with suitable numerical examples. The drawbacks of traditional tools for optimization will be stated. The working principle of one of the most popular non-traditional tools for optimization, namely genetic algorithm (GA) will be explained in detailed. Schema theorem of binary-coded GA will be discussed.
Lecture 1 Principle of Optimization
26mLecture 2 Traditional Methods of Optimization
29mLecture 3: Traditional Methods of Optimization (Contd.)
21mLecture 4 Binary-Coded Genetic Algorithm (BCGA)
28mLecture 5 Binary-Coded Genetic Algorithm (BCGA) Contd.
29mLecture 6 Binary-Coded Genetic Algorithm (BCGA) Contd.
29mLecture 7 Binary-Coded Genetic Algorithm (BCGA) Contd.
27mLecture 8 Binary-Coded Genetic Algorithm (BCGA) Contd.
18mLecture 9 Schema Theorem of BCGA
24mLecture 10: Schema Theorem of BCGA (Contd.)
15mLecture 11 : Constraint Handling
36mLecture 12 : Real-Coded GA
27mLecture 13 : Faster Genetic Algorithms
27mLecture 14 : Faster Genetic Algorithms (Contd.)
19mLecture 15 : Faster Genetic Algorithms (Contd.)
32mLecture 16 : Faster Genetic Algorithms (Contd.)
32mLecture 17 : Scheduling GA
21mLecture 18 : Scheduling GA (Contd.)
21mLecture 19 : Scheduling GA (Contd.)
23mLecture 20 : Simmulated Annealing
32mLecture 21 : Particle Swarm Optimization
30mLecture 22: Multi-Objective Optimization
26mLecture 23: Multi-Objective Optimization (Contd.)
27mLecture 24: Multi-Objective Optimization (Contd.)
31mLecture 25: Multi-Objective Optimization (Contd.)
32mLecture 26: Multi-Objective Optimization (Contd.)
15mLecture 27: Intelligent Optimization Toolture
18mLecture 28: A Practical Optimization Problem
18mLecture 29: A Practical Optimization Problem (Contd.)
26mLecture 30: A Practical Optimization Problem (Contd.)
28mLecture 31: A Practical Optimization Problem (Contd.)
21mLecture 32: A Practical Optimization Problem (Contd.)
32mLecture 33: A Practical Optimization Problem (Contd.)
13mLecture 34: A Practical Optimization Problem (Contd.)
14mLecture 35: A Practical Optimization Problem (Contd.)
14mLecture 36: A Practical Optimization Problem (Contd.)
27mLecture 37: Genetic Algorithm as Evolution Tool
27mLecture 38: Genetic Algorithm as Evolution Tool (Contd.)
28mLecture 39: Genetic Algorithm as Evolution Tool (Contd.)
27mLecture 40: Genetic Algorithm as Evolution Tool (Contd.)
27mLecture 41: Genetic Algorithm as Evolution Tool (Contd.)
29mLecture 42: Genetic Algorithm as Evolution Tool (Contd.)
31mLecture 43: Summary 1
29mLecture 44: Summary 2
34mLecture 45: Summary 3
32m