Efficient Evolution of High Entropy RNGs Using Single Node Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InProceedings{Leonard:2015:GECCO,

• author = "Philip Leonard and David Jackson",
• title = "Efficient Evolution of High Entropy RNGs Using Single Node Genetic Programming",
• booktitle = "GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation",
• year = "2015",
• editor = "Sara Silva and Anna I Esparcia-Alcazar and Manuel Lopez-Ibanez and Sanaz Mostaghim and Jon Timmis and Christine Zarges and Luis Correia and Terence Soule and Mario Giacobini and Ryan Urbanowicz and Youhei Akimoto and Tobias Glasmachers and Francisco {Fernandez de Vega} and Amy Hoover and Pedro Larranaga and Marta Soto and Carlos Cotta and Francisco B. Pereira and Julia Handl and Jan Koutnik and Antonio Gaspar-Cunha and Heike Trautmann and Jean-Baptiste Mouret and Sebastian Risi and Ernesto Costa and Oliver Schuetze and Krzysztof Krawiec and Alberto Moraglio and Julian F. Miller and Pawel Widera and Stefano Cagnoni and JJ Merelo and Emma Hart and Leonardo Trujillo and Marouane Kessentini and Gabriela Ochoa and Francisco Chicano and Carola Doerr",
• isbn13 = "978-1-4503-3472-3",
• pages = "1071--1078",
• keywords = "genetic algorithms, genetic programming",
• month = "11-15 " # jul,
• organisation = "SIGEVO",
• address = "Madrid, Spain",
• URL = "http://doi.acm.org/10.1145/2739480.2754820",
• DOI = "doi:10.1145/2739480.2754820",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Random Number Generators are an important aspect of many modern day software systems, cryptographic protocols and modelling techniques. To be more accurate, it is Pseudo Random Number Generators (PRNGs) that are more commonly used over their expensive, and less practical hardware based counterparts. Given that PRNGs rely on some deterministic algorithm (typically a Linear Congruential Generator) we can leverage Shannon's theory of information as our fitness function in order to generate these algorithms by evolutionary means. In this paper we compare traditional Genetic Programming (GP) against its graph based implementation, Single Node Genetic Programming (SNGP), for this task. We show that with SNGPs unique program structure and use of dynamic programming, it is possible to obtain smaller, higher entropy PRNGs, over six times faster and produced at a solution rate twice that achieved using Koza's standard GP model. We also show that the PRNGs obtained from evolutionary methods produce higher entropy outputs than other widely used PRNGs and Hardware RNGs (specifically recordings of atmospheric noise), as well as surpassing them in a variety of other statistical tests presented in the NIST RNG test suite.",
• notes = "Also known as \cite{2754820} GECCO-2015 A joint meeting of the twenty fourth international conference on genetic algorithms (ICGA-2015) and the twentith annual genetic programming conference (GP-2015)",

}

Genetic Programming entries for Philip Leonard David Jackson

Citations