Control System Synthesis by Means of Cartesian Genetic Programming
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- @Article{Balandina:2017:PCS,
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author = "G. I. Balandina",
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title = "Control System Synthesis by Means of Cartesian Genetic
Programming",
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journal = "Procedia Computer Science",
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volume = "103",
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pages = "176--182",
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year = "2017",
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note = "\{XII\} International Symposium Intelligent Systems
2016, \{INTELS\} 2016, 5-7 October 2016, Moscow,
Russia",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, Optimal control synthesis,
nonlinear control systems",
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ISSN = "1877-0509",
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DOI = "doi:10.1016/j.procs.2017.01.051",
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URL = "http://www.sciencedirect.com/science/article/pii/S1877050917300522",
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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
Galina Ivanova Balandina
Citations