This project explores the fascinating intersection of genetic algorithms and natural selection, demonstrating how computational concepts can mirror biological processes. The project delves into the basic building blocks of algorithms, their historical development, and their application in modern technology and natural processes.
This project serves as an educational exploration into the realm of algorithms, starting from their historical roots with figures like Al-Khwarizmi and George Boole, through to Alan Turing's development of the Turing machine, and onto their contemporary applications in technology and natural processes.
The core of this project investigates how algorithms are not just tools for computation but are also evident in natural processes, specifically through the lens of genetic algorithms in biology. These algorithms illustrate concepts like selection, crossover, and mutation, paralleling the processes of natural selection.
The included Python code provides a practical implementation of a genetic algorithm, showcasing how such algorithms can evolve solutions to complex problems, mimicking natural evolutionary strategies. The code is well-documented, providing insights into each step of the algorithm's operation.