image_reconstruction_genetic_algorithm

This project implements a Genetic Algorithm (GA) to approximate a target image using a grid of colored cells. Over successive generations, a population of randomly initialized individuals evolves to closely resemble the original image.

🧬 How It Works

Each individual (or chromosome) is a grid of RGB-colored cells. The GA tries to minimize the color difference between each cell and the corresponding region of the original image.

Genetic Algorithm Steps:

1. Initialization

   • Each chromosome is filled with random colors.
   • The target image is resized and divided into a grid based on CELL_SIZE.

2. Fitness Evaluation

   • The fitness function calculates the sum of squared differences between the RGB values of each cell and the average color of the corresponding region in the target image.

3. Selection

   • Roulette wheel selection is used to choose parents, giving preference to individuals with higher fitness.

4. Crossover

   • A uniform crossover combines two parent chromosomes by randomly selecting cells from each to form a new child.

5. Mutation

   • A small mutation rate causes slight changes in the color of some cells, nudging them toward the target region's color with added randomness.

6. Elitism

   • The best individuals are preserved in the next generation to maintain progress.

7. Visualization

   • Pygame displays the best chromosome of each generation in real time.

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🖼 Image Input

Make sure to specify the path to the target image in the script: