genetic-algorithms-ai

A Python GUI application demonstrating genetic algorithms and differential evolution for benchmark optimization.

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Overview

This repository implements evolutionary optimization methods and presents results in an interactive Tkinter interface.

The application supports:

• Genetic Algorithm optimization
• Differential Evolution optimization
• Benchmark functions including Booth, Matyas, Rosenbrock, Sphere, and Zakharov
• Visualization of convergence, diversity, and fitness trends
• Real-time GUI controls for algorithm parameters

Quick start

git clone https://github.com/ali-ezz/Genetic-Algorithms-in-AI.git
cd Genetic-Algorithms-in-AI
python3 -m venv .venv
source .venv/bin/activate
python3 -m pip install -r requirements.txt
python3 ProjectGA.py

Requirements

• Python 3.11 or newer
• numpy
• matplotlib
• tkinter (standard library GUI toolkit)

Usage

1. Launch the app with python3 ProjectGA.py.
2. Select an optimization algorithm and benchmark function.
3. Adjust population size, generation count, crossover rate, and mutation or differential weight.
4. Start the optimization run and review the results, plots, and performance metrics.

File layout

• ProjectGA.py — main application code with genetic algorithm and differential evolution implementations.
• README.md — project overview, setup, and usage.
• requirements.txt — Python dependency list.
• LICENSE — project license.
• CONTRIBUTING.md — contributor guide.
• CODE_OF_CONDUCT.md — contributor behavior policy.
• SECURITY.md — vulnerability reporting guidance.
• .github/ — GitHub issue, PR templates, and metadata.
• CHANGELOG.md — release notes.
• docs/overview.md — documentation and usage summary.

Contribution

See CONTRIBUTING.md for issue and pull request guidance.

License

This project is licensed under the MIT License.