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Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences

Adamopoulos, A V and Pavlidis, N and Vrahatis, M N (2010) Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences. Mathematical and Computer Modelling, 51 (3-4). pp. 229-238.

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Abstract

Complex binary sequences are generated through the application of simple threshold, linear transformations to the logistic iterative map. Depending primarily on the value of its non-linearity parameter, the logistic map exhibits a great variety of behavior, including stable states, cycling and periodical activity and the period doubling phenomenon that leads to high-order chaos. From the real data sequences, binary sequences are derived. Consecutive LL bit sequences are given as input to a cellular automaton with the task to regenerate the subsequent LL bits of the binary sequence in precisely LL evolution steps. To perform this task a genetic algorithm is employed to evolve cellular automaton rules. Various complex binary sequences are examined, for a variety of initial values and a wide range of values of the non-linearity parameter. The proposed hybrid multiple-step-ahead prediction algorithm, based on a combination of genetic algorithms and cellular automata proved efficient and effective.

Item Type: Article
Journal or Publication Title: Mathematical and Computer Modelling
Subjects:
Departments: Lancaster University Management School > Management Science
ID Code: 45671
Deposited By: ep_importer_pure
Deposited On: 11 Jul 2011 19:36
Refereed?: Yes
Published?: Published
Last Modified: 21 Sep 2017 04:30
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/45671

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