Using Python and Julia for Efficient Implementation of Natural Computing and Complexity Related Algorithms

Computational efficiency and several other criteria are investigated from the perspective of using Python and Julia languages when used in natural computing and complexity related algorithms. While such algorithms often require high computational power, portability and easiness of implementing various algorithms, it is important to identify freely available platforms for high performance, high portability and high productivity (HP3). Using several examples, we conclude that Python is a very good choice for researchers already fluent in either Matlab/Octave environments, while Julia, a newcomer with similar features to Python but less package offer the promise of better speed.