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  • Cartesian genetic programming (CGP) is a branch of genetic programming which has been utilized in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2-5 times in comparison with the standard CGP.
  • Cartesian genetic programming (CGP) is a branch of genetic programming which has been utilized in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2-5 times in comparison with the standard CGP. (en)
Title
  • Coevolution in Cartesian Genetic Programming
  • Coevolution in Cartesian Genetic Programming (en)
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  • Coevolution in Cartesian Genetic Programming
  • Coevolution in Cartesian Genetic Programming (en)
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  • RIV/00216305:26230/12:PU98164!RIV13-GA0-26230___
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  • P(ED1.1.00/02.0070), P(GAP103/10/1517), S, Z(MSM0021630528)
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  • 127593
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  • RIV/00216305:26230/12:PU98164
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  • Cartesian genetic programming, coevolution, fitness modeling, fitness predictors, symbolic regression. (en)
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  • [8DA806DD970F]
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  • Malaga
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  • Heidelberg
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  • Proc. of the 15th European Conference on Genetic Programming
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  • Sekanina, Lukáš
  • Šikulová, Michaela
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  • 10.1007/978-3-642-29139-5_16
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  • Springer-Verlag
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  • 978-3-642-29138-8
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  • 26230
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