Genetic Algorithm Implementation in Python | Step-by-Step Evolutionary Optimization

In this video, we’ll implement a **Genetic Algorithm** from scratch using Python! 🧬💻
This hands-on session builds on our previous explanation and shows how genetic principles like **selection**, **crossover**, and **mutation** can solve optimization problems.

✅ What You'll Learn:
- Defining the problem and fitness function
- Initializing a population
- Applying selection, crossover, and mutation
- Evolving the population across generations
- Analyzing final results and best solution

📌 Tools & Libraries Used:
- Python
- NumPy
- Matplotlib (for visualization, optional)

🎯 Whether you're building AI for optimization, game bots, or neural network tuning, this video gives you the practical foundation for Genetic Algorithms.

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