Created by W.Langdon from gp-bibliography.bib Revision:1.7810

- @Article{Ryan:2022:SciRep,
- author = "Conor Ryan and Meghana Kshirsagar and Gauri Vaidya and Andrew Cunningham and R. Sivaraman",
- title = "Design of a cryptographically secure pseudo random number generator with grammatical evolution",
- journal = "Scientific Reports",
- year = "2022",
- volume = "12",
- pages = "Article number: 8602",
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
- URL = "https://rdcu.be/cUXIn",
- DOI = "doi:10.1038/s41598-022-11613-x",
- size = "10 pages",
- abstract = "This work investigates the potential for using Grammatical Evolution (GE) to generate an initial seed for the construction of a pseudo-random number generator (PRNG) and cryptographically secure (CS) PRNG. We demonstrate the suitability of GE as an entropy source and show that the initial seeds exhibit an average entropy value of 7.940560934 for 8-bit entropy, which is close to the ideal value of 8. We then construct two random number generators, GE-PRNG and GE-CSPRNG, both of which employ these initial seeds. We use Monte Carlo simulations to establish the efficacy of the GE-PRNG using an experimental setup designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system. This returned the value of pi of 3.146564000, which is precise up to six decimal digits for the actual value of pi. We propose a new approach called control_flow_incrementor to generate cryptographically secure random numbers. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology SP800-22 and the Diehard statistical test requirements. We also present a computational performance analysis of GE-CSPRNG demonstrating its potential to be used in industrial applications.",
- }

Genetic Programming entries for Conor Ryan Meghana Kshirsagar Gauri Vaidya Andrew Cunningham R Sivaraman