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

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Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences. / Adamopoulos, A V; Pavlidis, N; Vrahatis, M N.
In: Mathematical and Computer Modelling, Vol. 51, No. 3-4, 2010, p. 229-238.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Adamopoulos, AV, Pavlidis, N & Vrahatis, MN 2010, 'Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences', Mathematical and Computer Modelling, vol. 51, no. 3-4, pp. 229-238. https://doi.org/10.1016/j.mcm.2009.08.010

APA

Adamopoulos, A. V., Pavlidis, N., & Vrahatis, M. N. (2010). Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences. Mathematical and Computer Modelling, 51(3-4), 229-238. https://doi.org/10.1016/j.mcm.2009.08.010

Vancouver

Adamopoulos AV, Pavlidis N, Vrahatis MN. Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences. Mathematical and Computer Modelling. 2010;51(3-4):229-238. doi: 10.1016/j.mcm.2009.08.010

Author

Adamopoulos, A V ; Pavlidis, N ; Vrahatis, M N. / Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences. In: Mathematical and Computer Modelling. 2010 ; Vol. 51, No. 3-4. pp. 229-238.

Bibtex

@article{fcc1367639bc4bebad9ab6951193202b,
title = "Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences",
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.",
author = "Adamopoulos, {A V} and N Pavlidis and Vrahatis, {M N}",
year = "2010",
doi = "10.1016/j.mcm.2009.08.010",
language = "English",
volume = "51",
pages = "229--238",
journal = "Mathematical and Computer Modelling",
publisher = "Elsevier Limited",
number = "3-4",

}

RIS

TY - JOUR

T1 - Evolving cellular automata rules for multiple-step-ahead prediction of complex binary sequences

AU - Adamopoulos, A V

AU - Pavlidis, N

AU - Vrahatis, M N

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1016/j.mcm.2009.08.010

DO - 10.1016/j.mcm.2009.08.010

M3 - Journal article

VL - 51

SP - 229

EP - 238

JO - Mathematical and Computer Modelling

JF - Mathematical and Computer Modelling

IS - 3-4

ER -