Control System Synthesis by Means of Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Balandina:2017:PCS,
  author =       "G. I. Balandina",
  title =        "Control System Synthesis by Means of Cartesian Genetic
                 Programming",
  journal =      "Procedia Computer Science",
  volume =       "103",
  pages =        "176--182",
  year =         "2017",
  note =         "\{XII\} International Symposium Intelligent Systems
                 2016, \{INTELS\} 2016, 5-7 October 2016, Moscow,
                 Russia",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Optimal control synthesis,
                 nonlinear control systems",
  ISSN =         "1877-0509",
  DOI =          "doi:10.1016/j.procs.2017.01.051",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1877050917300522",
  abstract =     "Cartesian Genetic Programming (CGP) is a type of
                 Genetic Programming based on a program in a form of a
                 directed graph. It also belongs to the methods of
                 Symbolic Regression allowing to receive the optimal
                 mathematical expression for a problem. Nowadays it
                 becomes possible to use computers very effectively for
                 symbolic regression calculations. CGP was developed by
                 Julian Miller in 1999-2000. It represents a program for
                 decoding a genotype (string of integers) into the
                 phenotype (graph). The nodes of that graph contain
                 references to functions from a function table, which
                 could contain arithmetic, logical operations and/or
                 user-defined functions. The inputs of those functions
                 are connected to the node inputs, which itself could be
                 connected to a node output or a graph input. As a
                 result, it's possible to construct several mathematical
                 expressions for the outputs and calculate them for the
                 given inputs. This CGP implementation use point
                 mutation to form new mathematical expressions.
                 Steady-state genetic algorithm is chosen as a search
                 engine. Solution solving the control system synthesis
                 problem is presented in a form of the Pareto set, which
                 contains a set of satisfactory control functions.
                 Nonlinear Duffing oscillator is taken as a dynamic
                 object.",
}

Genetic Programming entries for G I Balandina

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