Artificial Life Approach for Continuous Optimisation of Non Stationary Dynamical Systems

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@Article{AnnunziatoL2003:ICAE,
  author =       "Mauro Annunziato and Carlo Bruni and 
                 Matteo Lucchetti and Stefano Pizzuti",
  title =        "Artificial Life Approach for Continuous Optimisation
                 of Non Stationary Dynamical Systems",
  journal =      "Integrated Computer-Aided Engineering",
  year =         "2003",
  volume =       "10",
  number =       "2",
  pages =        "111--125",
  email =        "lucchetti@dis.uniroma1.it",
  keywords =     "genetic algorithms, genetic programming, artificial
                 life",
  ISSN =         "1069-2509",
  URL =          "http://content.iospress.com/articles/integrated-computer-aided-engineering/ica00140",
  size =         "15 pages",
  abstract =     "In this paper, we develop an intelligent system to
                 approach dynamical optimisation problems emerging in
                 control of complex systems. In particular our proposal
                 is to exploit the adaptivity of an artificial life
                 (alife) environment in order to achieve 'not control
                 rules but autonomous structures able to dynamically
                 adapt and to generate optimised-control rules'. The
                 basic features of the proposed approach are: no
                 intensive modelling (continuous learning directly from
                 measurements) and capability to follow the system
                 evolution (adaptation to environmental changes). The
                 suggested methodology has been tested on an energy
                 regulation problem deriving from a classical testbed in
                 dynamical systems experimentations: the Chua's circuit.
                 We supposed not to know the system dynamics and to be
                 able to act only on a subset of control parameters,
                 letting the others vary in time in a random discrete
                 way. We let the optimisation process searching for the
                 new best value of performance, whenever a drop due to
                 changes in fitness landscape occurred. We present the
                 most important results showing the effectiveness of the
                 proposed approach in adapting to environmental
                 non-stationary changes by recovering the optimal value
                 of process performance.",
}

Genetic Programming entries for Mauro Annunziato Carlo Bruni Matteo Lucchetti Stefano Pizzuti

Citations