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

Authors:
A. V. Adamopoulos;N. G. Pavlidis;M. N. Vrahatis
Affiliations:
Medical Physics Laboratory, Department of Medicine, Democritus University of Thrace, GR-681 00 Alexandroupolis, Greece;Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR-26110 Patras, Greece;Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, GR-26110 Patras, Greece
Venue:
Mathematical and Computer Modelling: An International Journal
Year:
2010

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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 L bit sequences are given as input to a cellular automaton with the task to regenerate the subsequent L bits of the binary sequence in precisely L 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.