Bacterially inspired evolving system with an application to time series prediction

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@Article{Rolania:2013:ASC,
  author =       "D. Barrios Rolania and J. M. Font and D. Manrique",
  title =        "Bacterially inspired evolving system with an
                 application to time series prediction",
  journal =      "Applied Soft Computing",
  volume =       "13",
  number =       "2",
  pages =        "1136--1146",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Natural
                 computing, Bio-inspired computation, Synthetic
                 biology",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2012.10.012",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494612004632",
  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