Genetic Algorithm

Automation of Genetic Algorithm operators and methods Presentation.
Automation of Genetic Algorithm operators and methods Thesi Docs

Project Report on Design of Generic GA

1. Analysis {Requirement Specification}
2. Design
3. Coding.
4. Testing.
5. Maintenance.

Analysis.
1. Purpose.
2. Hardware Environment.
3. Software Environment.
4. Why GA.
5. Why Generic GA.
6. Ideas.
7. Uses.
8. Pros and cons.
9. Available Software Environments.
10. Portability across various platforms.
11. Analysis of Convergence.
a. Types of optimization problems.
b. Does it converge for all problems?
c. How to increase the convergence rate.
d. Check for convergence.
e. How to check if the problem has converged.

12. Types of Optimization problems.
13. Types of tools for solving Optimization problems.
14. 14. Advantages of Solving Optimization problem using GA.
15. Types of population Representation of problems in GA.
16. GA Algorithm
a. Initialization.
b. Selection
c. Crossover.
d. Mutation.
e. Termination.
17. Types of Initialization and termination condition.
18. Types of Selection, Crossover, Mutation.
19. Analysis of simple problems.
20. GA Optimization parameters.
21. Generic part vs. problem specific parts.
22. Types of representation of generic GA.
23. Optimization of Generic parts.
24. Problem solution through software libraries.
25. Multi-Client and Multi-Servers problem solution.
26. Optimization of Problem Specific parts.
27. Optimization of Algorithm to conserve space and time.
28. Data structure representation of Client and Server.