Computer vision algorithms that make use of optical flow are constantly increasing in complexity, especially in the context of elaborated algorithms that are heavily inspired by biology. To develop upon and utilise these algorithms for realworld tasks, their extensive parameter sets need to be tuned. Due to algorithmic complexity, and non-linearities present throughout their parameter set, this is no small task. Using an adaptive genetic algorithm, which itself is biologically-inspired, we look at the performance and behaviour of the tuning when significant changes have been made to a low speed rotational velocity optical flow estimation algorithm. We validate that previously reported changes to the optical flow estimator yielded a fitness increase of over 30% when compared to the baseline model the changes were made to, and a 15% increase once that baseline model was also tuned. This improvement would be extremely unlikely without the aid of evolutionary computation algorithms. This shows that even an extremely complex computer vision algorithm with many parameters, 36 in this case, can be tuned to an operating point, facilitating the continued development work on the vision algorithm by allowing for the validation and quantification of algorithmic changes.