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In this project, an attempt is made to mimic a visual image using genetic algorithms.

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Genetic Algorithm for Visual Imitation

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.

Examples

IN OUT
in1 out1
K 80
Initial Population Size 1000
Number of Generations 1000
Number of Best Individuals 100
Mutation Rate 0.5
IN OUT
in2 out2
K 80
Initial Population Size 1000
Number of Generations 1000
Number of Best Individuals 100
Mutation Rate 0.5
IN OUT
in3 out3
K 80
Initial Population Size 1000
Number of Generations 1000
Number of Best Individuals 100
Mutation Rate 0.5
IN OUT
in4 out4
K 80
Initial Population Size 1000
Number of Generations 1000
Number of Best Individuals 100
Mutation Rate 0.5

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In this project, an attempt is made to mimic a visual image using genetic algorithms.

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