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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. |
URI: | http://bura.brunel.ac.uk/handle/2438/1109 |
ISSN: | 1089-778X |
Appears in Collections: | Electronic and Electrical Engineering Dept of Electronic and Electrical Engineering Research Papers |
Files in This Item:
File | Description | Size | Format | |
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Evolutionary approach 2003.pdf | 1.03 MB | Adobe PDF | View/Open |
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