Improving the Quality of Random Number Generators by Applying a Simple Ratio Transformation

TL;DR

This paper proposes using the ratio of two random numbers to improve generator quality, showing both theoretical soundness and empirical success in passing rigorous randomness tests, especially for weaker generators.

Contribution

The paper introduces a ratio transformation method for random number generators, demonstrating its effectiveness in significantly enhancing their statistical quality.

Findings

Over half of moderately bad generators pass all tests after ratio transformation.

The ratio approach breaks regularities in linear generators, improving randomness.

Empirical results confirm the theoretical approximation to uniform distribution.

Abstract

It is well-known that the quality of random number generators can often be improved by combining several generators, e.g. by summing or subtracting their results. In this paper we investigate the ratio of two random number generators as an alternative approach: the smaller of two input random numbers is divided by the larger, resulting in a rational number from $[0,1]$. We investigate theoretical properties of this approach and show that it yields a good approximation to the ideal uniform distribution. To evaluate the empirical properties we use the well-known test suite \textsc{TestU01}. We apply the ratio transformation to moderately bad generators, i.e. those that failed up to 40\% of the tests from the test battery \textsc{Crush} of \textsc{TestU01}. We show that more than half of them turn into very good generators that pass all tests of \textsc{Crush} and \textsc{BigCrush} from \textsc{TestU01} when the ratio transformation is applied. In particular, generators based on linear operations seem to benefit from the ratio, as this breaks up some of the unwanted regularities in the input sequences. Thus the additional effort to produce a second random number and to calculate the ratio allows to increase the quality of available random number generators.