Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Michael A. Lones and Luis A. Fuente and 
                 Alexander P. Turner and Leo S. D. Caves and Susan Stepney and 
                 Stephen L. Smith and Andy M. Tyrrell",
  journal =      "IEEE Transactions on Evolutionary Computation",
  title =        "Artificial Biochemical Networks: Evolving Dynamical
                 Systems to Control Dynamical Systems",
  year =         "2014",
  volume =       "18",
  number =       "2",
  pages =        "145--166",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Biochemical
                 networks, Chaos control, Dynamical systems,
                 Evolutionary robotics",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2013.2243732",
  size =         "22 pages",
  abstract =     "Biological organisms exist within environments in
                 which complex, non-linear dynamics are ubiquitous. They
                 are coupled to these environments via their own
                 complex, dynamical networks of enzyme-mediated
                 reactions, known as biochemical networks. These
                 networks, in turn, control the growth and behaviour of
                 an organism within its environment. In this paper, we
                 consider computational models whose structure and
                 function are motivated by the organisation of
                 biochemical networks. We refer to these as artificial
                 biochemical networks, and show how they can be evolved
                 to control trajectories within three behaviourally
                 diverse complex dynamical systems: the Lorenz system,
                 Chirikovs standard map, and legged robot locomotion.
                 More generally, we consider the notion of evolving
                 dynamical systems to control dynamical systems, and
                 discuss the advantages and disadvantages of using
                 higher order coupling and configurable dynamical
                 modules (in the form of discrete maps) within
                 artificial biochemical networks. We find both
                 approaches to be advantageous in certain situations,
                 though note that the relative trade-offs between
                 different models of artificial biochemical network
                 strongly depend on the type of dynamical systems being
  notes =        "Also known as \cite{6423886}",

Genetic Programming entries for Michael A Lones Luis A Fuente Alexander P Turner Leo Caves Susan Stepney Stephen L Smith Andrew M Tyrrell