This project implements a fruit (pomegranate) recognition system optimized using a Genetic Algorithm (GA). The objective is to apply evolutionary optimization techniques to improve classification performance in a fruit recognition task.

The project demonstrates how Genetic Algorithms can be integrated with machine learning workflows for feature selection, parameter tuning, or performance optimization.

Fruit recognition is a computer vision and classification problem. In this project, a Genetic Algorithm is used to:

• Optimize feature selection
• Tune model parameters
• Improve classification accuracy
• Reduce unnecessary complexity

Each candidate solution (chromosome) is evaluated using a fitness function based on classification performance. Over multiple generations, the population evolves toward better-performing solutions.

• Genetic Algorithms (Selection, Crossover, Mutation)
• Fitness Function Design
• Supervised Learning
• Model Evaluation Metrics
• Optimization Techniques

main.py
train.py
dataset/
README.md

1. Initialize a random population
2. Evaluate fitness of each individual
3. Select top-performing individuals
4. Apply crossover to create offspring
5. Apply mutation to maintain diversity
6. Form new generation
7. Repeat until convergence or max generations reached

• Python 3.9+

Common dependencies:

• numpy
• pandas
• matplotlib
• scikit-learn
• opencv-python (if image processing is included)

Install required packages:

pip install numpy pandas matplotlib scikit-learn opencv-python

Execute the main script:

python train.py
python main.py

Expected workflow:

• Load dataset
• Initialize genetic algorithm
• Train and evaluate model
• Output accuracy and performance metrics

If using image data, a typical structure is:

dataset/
    inputs/
    outputs/
    train/

Each folder contains labeled images for that fruit class.

The model may be evaluated using:

• Accuracy
• Precision
• Recall
• F1-Score
• Confusion Matrix

• Add cross-validation
• Implement elitism strategy
• Add logging and experiment tracking
• Add configuration file for hyperparameters
• Improve mutation and selection strategies
• Add visualization of GA convergence

• Automated fruit sorting systems
• Agricultural quality control
• Educational demonstration of evolutionary algorithms
• Optimization research projects

Mahan Baneshi

Developed as an academic and practical implementation of Genetic Algorithms applied to a fruit recognition problem.