Build a Self-Evolving Genetic Algorithm in Python | Step-by-Step Beginner Tutorial

In this video, you will learn how to build a self-evolving genetic algorithm in Python, step by step! Perfect for beginners and those new to coding, this project introduces the core concepts of genetic algorithms and walks you through creating a Python program that evolves over generations to solve problems—specifically the Fibonacci sequence problem.

What You'll Learn:
✅ What are genetic algorithms, and how do they mimic evolution?
✅ How to generate an initial population of solutions
✅ How to create a fitness function to measure solution effectiveness
✅ The selection, crossover, and mutation processes
✅ How to evolve a population over generations to solve problems

By the end of this tutorial, you’ll have created a simple, self-evolving genetic algorithm that not only solves a problem but also enhances your understanding of problem-solving and algorithms in Python.

Code Features:
- Beginner-friendly explanations
- No additional libraries—use only built-in Python functions

Chapters:
0:00 - Introduction
0:38 - What is a Genetic Algorithm?
1:25 - The Fibonacci Sequence Explained
2:18 - Setting Up Your Development Environment
2:55 - Generating the Initial Population (Code)
7:42 - The Fitness Function Defined and Explained (Code)
12:16 - The Selection Process (Code)
14:54 - Crossover and Mutation (Code)
20:45 - Implementing the Genetic Algorithm (Evolving the Population over Generations)
26:45 - Running the Program and Conclusion

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