| |
| Course Content |
Time Allocated |
| L |
T |
P |
A |
Introduction
- Terminology Concept of natural evolution, Biological terminology
|
2 |
|
|
|
Genetic Algorithm
- Basic genetic operators; reproduction, crossover, mutation; computer implementation, size of populations, applications
|
6 |
2 |
|
|
Numerical Optimization
- Binary and floating point representations, Fine local tuning, Handling constrained problems, Applications
|
6 |
2 |
|
|
Evolution Strategies
- Comparision of evolution strategies and genetic algorithms, Multimodel optimization, Multiobjective optimization
|
2 |
|
|
|
Evolution Programs
- Evolution Programs for discrete problems, Machine learning, Evolution programs and heuristics, Applications
|
8 |
2 |
|
|
| Total = 24 + 6 = 30 |
24 |
6 |
|
|
| |
| Assessment |
Percentage Mark |
| Continuous Assessment |
|
100 |
| Assignment |
40 |
|
| Course work |
60 |
|
| Written Examinations |
|
|
| Mid-Semester |
|
|
| End of Semester |
|
|
|
Notation Used :
L - Lectures
T - Tutorials
P - Practical works
A - Assignments |