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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/1133

Title: Incremental evolution of cellular automata for random number generation
Authors: Guan, SU
Zhang, S
Keywords: Controllable cellular automata
Pseudorandom number generation
Multi-objective optimization
Incremental evolution
Publication Date: 2003
Publisher: World Scientific Publishing
Citation: International Journal of Modern Physics C. 14 (07): 881-896, Sep 2003
Abstract: Cellular automata (CA) have been used in pseudorandom number generation for over a decade. Recent studies show that controllable CA (CCA) can generate better random sequences than conventional one-dimensional (1-d) CA and compete with two-dimensional (2-d) CA. Yet the structural complexity of CCA is higher than that of 1-d PCA. It would be good if CCA can attain good randomness quality with the least structural complexity. In this paper, we evolve PCA/CCA to their lowest complexity level using genetic algorithms (GAs). Meanwhile, the randomness quality and output efficiency of PCA/CCA are also evolved. The evolution process involves two algorithms  a multi-objective genetic algorithm (MOGA) and an algorithm for incremental evolution. A set of PCA/CCA are evolved and compared in randomness, complexity, and efficiency. The results show that without any spacing, CCA could generate good random number sequences that could pass DIEHARD. And, to obtain the same randomness quality, the structural complexity of CCA is not higher than that of 1-d CA. Furthermore, the methodology developed could be used to evolve other CA or serve as a yardstick to compare different types of CA.
URI: http://bura.brunel.ac.uk/handle/2438/1133
ISSN: 0129-1831
Appears in Collections:School of Engineering and Design Research papers
Electronic and Computer Engineering

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