This project explores the visual imitation capabilities of genetic algorithms. The application takes a black and white image as input and attempts to recreate it using a genetic algorithm.
The algorithm initially distributes a uniform points randomly in an m x m area. Each gene used by the genetic algorithm is composed of a combination of these points. The image is imitated by connecting these points and drawing paths.
The project provides a dynamic visualization screen that allows you to visually see the best result of each generation.
The parameters of the algorithm are as follows:
k: Determines the number of points used to create the image.
Initial Population Size: The size of the initial population is provided as a parameter.
Number of Generations: The number of generations is provided as a parameter.
Number of Best Individuals: The number of best individuals to be selected from the initial population is provided as a parameter.
Mutation Rate: The mutation rate is provided as a parameter.
This project is a fascinating exploration of how genetic algorithms can be used in the field of image processing and computer graphics. It demonstrates the power of evolutionary computation in tackling complex problems and generating creative solutions.
| IN | OUT |
|---|---|
 |
 |
| K | 80 |
| Initial Population Size | 1000 |
| Number of Generations | 1000 |
| Number of Best Individuals | 100 |
| Mutation Rate | 0.5 |
| IN | OUT |
|---|---|
 |
 |
| K | 80 |
| Initial Population Size | 1000 |
| Number of Generations | 1000 |
| Number of Best Individuals | 100 |
| Mutation Rate | 0.5 |
| IN | OUT |
|---|---|
 |
 |
| K | 80 |
| Initial Population Size | 1000 |
| Number of Generations | 1000 |
| Number of Best Individuals | 100 |
| Mutation Rate | 0.5 |
| IN | OUT |
|---|---|
 |
 |
| K | 80 |
| Initial Population Size | 1000 |
| Number of Generations | 1000 |
| Number of Best Individuals | 100 |
| Mutation Rate | 0.5 |