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  • This paper describes an efficient acceleration technique designed to speedup the evaluation of candidate solutions in Cartesian Genetic Programming (CGP). The method is based on translation of the CGP phenotype to a binary machine code that is consequently executed. The key feature of the presented approach is that the introduction of the translation mechanism into common fitness evaluation procedure requires only marginal knowledge of target CPU instruction set. The proposed acceleration technique is evaluated using a symbolic regression problem in floating point domain. It is shown that for a cost of small changes in a common CGP implementation, a significant speedup can be obtained even on a common desktop CPU.
  • This paper describes an efficient acceleration technique designed to speedup the evaluation of candidate solutions in Cartesian Genetic Programming (CGP). The method is based on translation of the CGP phenotype to a binary machine code that is consequently executed. The key feature of the presented approach is that the introduction of the translation mechanism into common fitness evaluation procedure requires only marginal knowledge of target CPU instruction set. The proposed acceleration technique is evaluated using a symbolic regression problem in floating point domain. It is shown that for a cost of small changes in a common CGP implementation, a significant speedup can be obtained even on a common desktop CPU. (en)
Title
  • Efficient Phenotype Evaluation in Cartesian Genetic Programming
  • Efficient Phenotype Evaluation in Cartesian Genetic Programming (en)
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  • Efficient Phenotype Evaluation in Cartesian Genetic Programming
  • Efficient Phenotype Evaluation in Cartesian Genetic Programming (en)
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  • RIV/00216305:26230/12:PU101812!RIV13-GA0-26230___
http://linked.open...avai/predkladatel
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  • P(ED1.1.00/02.0070), P(GAP103/10/1517), Z(MSM0021630528)
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  • 133571
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  • RIV/00216305:26230/12:PU101812
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  • genetic algorithms, genetic programming, cartesian genetic programming, Fitness evaluation, Acceleration, Symbolic regression (en)
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  • [72E424A10180]
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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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  • Vašíček, Zdeněk
  • Slaný, Karel
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number of pages
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  • Springer-Verlag
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  • 978-3-642-29138-8
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  • 26230
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