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@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