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Title: An Evolutionary Approach to the Design of Controllable Cellular Automata Structure for Random Number Generation
Authors: Guan, SU
Zhang, S
Keywords: genetic algorithms, multi-objective optimization, controllable cellular automata
Issue Date: 2003
Publisher: IEEE
Citation: Sheng-Uei Guan and Shu Zhang, “An Evolutionary Approach to the Design of Controllable Cellular Automata Structure for Random Number Generation”, 23-36, Vol. 7, No. 1, IEEE Trans. on Evolutionary Computation, Feb. 2003
Abstract: Cellular Automata (CA) has been used in pseudorandom number generation over a decade. Recent studies show that two-dimensional (2-d) CA Pseudorandom Number Generators (PRNGs) may generate better random sequences than conventional one-dimensional (1-d) CA PRNGs, but they are more complex to implement in hardware than 1-d CA PRNGs. In this paper, we propose a new class of 1-d CA  Controllable Cellular Automata (CCA) without much deviation from the structure simplicity of conventional 1-d CA. We give a general definition of CCA first and then introduce two types of CCA – CCA0 and CCA2. Our initial study on them shows that these two CCA PRNGs have better randomness quality than conventional 1-d CA PRNGs but their randomness is affected by their structures. To find good CCA0/CCA2 structures for pseudorandom number generation, we evolve them using the Evolutionary Multi-Objective Optimization (EMOO) techniques. Three different algorithms are presented in this paper. One makes use of an aggregation function; the other two are based on the Vector Evaluated Genetic Algorithm (VEGA). Evolution results show that these three algorithms all perform well. Applying a set of randomness tests on the evolved CCA PRNGs, we demonstrate that their randomness is better than that of 1-d CA PRNGs and can be comparable to that of two-dimensional CA PRNGs.
ISSN: 1089-778X
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Research Papers

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