Author(s)

Jose J. Mijares Chan¹, Parimala Thulasiraman², Gabriel Thomas¹, Ruppa Thulasiram²

¹Electrical and Computer Engineering Department-Faculty of Engineering, University of Manitoba, Winnipeg, Canada.
²Computer Science Department, Faculty of Science, University of Manitoba, Winnipeg, Canada.

Random numbers generated by pseudo-random and true random number generators (TRNG) are used in a wide variety of important applications. A TRNG relies on a non-deterministic source to sample random numbers. In this paper, we improve the post-processing stage of TRNGs using a heuristic evolutionary algorithm. Our post-processing algorithm decomposes the problem of improving the quality of random numbers into two phases: (i) Exact Histogram Equalization: it modifies the random numbers distribution with a specified output distribution; (ii) Stationarity Enforcement: using genetic algorithms, the output of (ii) is permuted until the random numbers meet wide-sense stationarity. We ensure that the quality of the numbers generated from the genetic algorithm is within a specified level of error defined by the user. We parallelize the genetic algorithm for improved performance. The post-processing is based on the power spectral density of the generated numbers used as a metric. We propose guideline parameters for the evolutionary algorithm to ensure fast convergence, within the first 100 generations, with a standard deviation over the specified quality level of less than 0.45. We also include a TestU01 evaluation over the random numbers generated.

True Random Number Generators, Genetic Algorithms, Auto-Correlation, Entropy, Power Spectral Density

Chan, J. , Thulasiraman, P. , Thomas, G. and Thulasiram, R. (2016) Ensuring Quality of Random Numbers from TRNG: Design and Evaluation of Post-Processing Using Genetic Algorithm. Journal of Computer and Communications, 4, 73-92. doi: 10.4236/jcc.2016.44007.