Bacterially inspired evolving system with an application to time series prediction
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- @Article{Rolania:2013:ASC,
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author = "D. Barrios Rolania and J. M. Font and D. Manrique",
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title = "Bacterially inspired evolving system with an
application to time series prediction",
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journal = "Applied Soft Computing",
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volume = "13",
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number = "2",
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pages = "1136--1146",
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year = "2013",
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keywords = "genetic algorithms, genetic programming, Natural
computing, Bio-inspired computation, Synthetic
biology",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2012.10.012",
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URL = "http://www.sciencedirect.com/science/article/pii/S1568494612004632",
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abstract = "This paper explores the synergies between evolutionary
computation and synthetic biology, developing an in
silico evolutionary system that is inspired by the
behaviour of bacterial populations living in
continuously changing environments. This system creates
a 3D environment seeded with a simulated population of
bacteria that eat, reproduce, interact with each other
and with the environment and eventually die. This
provides a 3D framework implementing an evolutionary
process. The subject of the evolution is each
bacterium's internal process, defining its interactions
with the environment. The evolutionary goal is the
survival of the population under successive,
continuously changing environmental conditions. The key
advantage of this bacterial evolutionary system is its
decentralised, asynchronous, parallel and self-adapting
general-purpose evolutionary process. We describe this
system and present the results of an application to the
evolution of a bacterial population that learns how to
predict the presence or absence of food in the
environment by analysing three input signals from the
environment. The resulting populations successfully
evolve by continuously improving their fitness under
different environmental conditions, demonstrating their
adaptability to a fluctuating medium.",
- }
Genetic Programming entries for
Dolores Barrios Rolania
Jose M Font
Daniel Manrique Gamo
Citations