A Metric for Evolving 2-D Cellular Automata As Pseudo-Random Number Generators
Identifieur interne : 006C50 ( Hal/Corpus ); précédent : 006C49; suivant : 006C51A Metric for Evolving 2-D Cellular Automata As Pseudo-Random Number Generators
Auteurs : Nikolaos Vlassopoulos ; Bernard GirauSource :
- Journal of Cellular Automata [ 1557-5969 ] ; 2014.
Abstract
In this paper we study the problem of evolving 2-dimensional Cellular Automata (CA) as Pseudo-random Number Generators (PRNG). First, we introduce a composite fitness metric that incorporates elements from PRNG tests, and which is more suitable for evolving CA. Second, we apply and verify this composite metric on two different use-cases: First, to evolve Additive CA as PRNGs using Genetic Algorithms and second, to evolve Totalistic CA as PRNGs using a Markov Chain Monte-Carlo approach.
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<front><div type="abstract" xml:lang="en">In this paper we study the problem of evolving 2-dimensional Cellular Automata (CA) as Pseudo-random Number Generators (PRNG). First, we introduce a composite fitness metric that incorporates elements from PRNG tests, and which is more suitable for evolving CA. Second, we apply and verify this composite metric on two different use-cases: First, to evolve Additive CA as PRNGs using Genetic Algorithms and second, to evolve Totalistic CA as PRNGs using a Markov Chain Monte-Carlo approach.</div>
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<abstract xml:lang="en">In this paper we study the problem of evolving 2-dimensional Cellular Automata (CA) as Pseudo-random Number Generators (PRNG). First, we introduce a composite fitness metric that incorporates elements from PRNG tests, and which is more suitable for evolving CA. Second, we apply and verify this composite metric on two different use-cases: First, to evolve Additive CA as PRNGs using Genetic Algorithms and second, to evolve Totalistic CA as PRNGs using a Markov Chain Monte-Carlo approach.</abstract>
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