![]() We'll also see how to implement different NumPy functions in C-speed, and will conclude the post with the final implementation of the full code and a comparison if its comparison with Python. Then we'll look at cythonizing each part of the genetic algorithm the fitness function, mating pool, crossover, and mutation. We'll begin by downloading the GitHub project. We'll inspect the code and follow the instructions discussed in the previous two tutorials to make as many changes as possible to boost performance, and the run the generations in significantly less time compared to Python. This tutorial builds upon what we discussed previously to speed-up the execution of a project that implements the genetic algorithm (GA) in Python. For example, when applied to NumPy arrays, Cython completed the sum of 1 billion numbers 1250 times faster than Python. This boosts the performance of Python scripts, resulting in dramatic speed increases.
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