Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata
Created by W.Langdon from
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- @Article{Weinert20106,
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author = "Wagner Rodrigo Weinert and Heitor Silverio Lopes",
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title = "Evaluation of dynamic behavior forecasting parameters
in the process of transition rule induction of
unidimensional cellular automata",
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journal = "Biosystems",
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volume = "99",
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number = "1",
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pages = "6--16",
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year = "2010",
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ISSN = "0303-2647",
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DOI = "doi:10.1016/j.biosystems.2009.08.002",
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URL = "http://www.sciencedirect.com/science/article/B6T2K-4X0XFDD-1/2/0604807ff3e25dde5b1b6902b792e157",
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keywords = "genetic algorithms, genetic programming, Cellular
automata, Dynamic behavior forecasting parameters,
Dynamic systems, Evolutionary computation",
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abstract = "The simulation of the dynamics of a cellular systems
based on cellular automata (CA) can be computationally
expensive. This is particularly true when such
simulation is part of a procedure of rule induction to
find suitable transition rules for the CA. Several
efforts have been described in the literature to make
this problem more treatable. This work presents a study
about the efficiency of dynamic behaviour forecasting
parameters (DBFPs) used for the induction of transition
rules of CA for a specific problem: the classification
by the majority rule. A total of 8 DBFPs were analysed
for the 31 best-performing rules found in the
literature. Some of these DBFPs were highly correlated
each other, meaning they yield the same information.
Also, most rules presented values of the DBFPs very
close each other. An evolutionary algorithm, based on
gene expression programming, was developed for finding
transition rules according a given preestablished
behavior. The simulation of the dynamic behavior of the
CA is not used to evaluate candidate transition rules.
Instead, the average values for the DBFPs were used as
reference. Experiments were done using the DBFPs
separately and together. In both cases, the best
induced transition rules were not acceptable solutions
for the desired behavior of the CA. We conclude that,
although the DBFPs represent interesting aspects of the
dynamic behavior of CAs, the transition rule induction
process still requires the simulation of the dynamics
and cannot rely only on the DBFPs.",
- }
Genetic Programming entries for
Wagner R Weinert
Heitor Silverio Lopes
Citations