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.

The General Genetic Algorithm Tool

IGNOU/DOEACC Free Sample Project Reports


Gray code used in Genetic Algorithm

1) Complete presentations on genetic algorithm and how they are
being used to solve the Travelling Salesman problem.
2) What are the different types of GA we have.
3) What are the different operators we have.
4) Application of GA's
5) How GA's are used to solve the TS problem.
a)Backgound
b) Which GA is used.
c) What are the operators used.
d) Algorithm itself.
e) Itself.

6)Terminology
a) Population.
b) Mutation rate.
c) Crossover rate.
d) Initial size of the population.
e) Terminating condition.
f) Population selection procedure.
g) Role of operators being used (which operators ).
h) Chromosome representation.
i) Generation cycle.

7)Plot the results
a) With reference to size of poplulation with respect
to computation time.
b) By changing crossover rate and mutation rate.
c) Changing the representation of chromosomes.


Name of thesis

OPtimised GA for Travelling salesman problem



Project Reports

Links

Link 1

1. Link 2

No Free Lunch Theorems for Optimization
Genetic Algorithm Publications

Genetic algorithm notes and research

Reasearch Crossover rate Mutation rate

Genetic Algorithm sensitivity to various parameters

Genetic Algorithm good notes

SENSE Crossover operator for Genetic Algorithms

From Genetic Algorithms
To Efficient Optimization

Global Optimization Techniques

Excellent GA Tutorial (Directed Random search)

Excellent slides on GA NP Hardproblems representation of GA etc


Exelixis: A parallel generic Genetic Algorithm

Genetic Algorithm, Fractals,Data Mining and other stuff

Genetic Algorithm source code

Genetic Algorithm project reports on various topics

Genetic Algorithm Hello World Program.

Simple way of Solving Travelling Salesman Problems Using Genetic Algorithms


Genetic Algorithm Question bank

Genetic Algorithms programming useful tools.

Getting exponent and mantissa from a floating point number



Evaluating Expressions program

Evaluating Expressions program (Programmers heaven)

Evaluating Expressions program

Representation of floating point numbers

QuickWin - Turn a console application into a Windows program

Float to byte conversion


Simple expression evaluator in visual C++

Simple expression evaluator in c++

Very good expression evaluator with variable handling in C++