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Accelerated Genetic Programming

  • Vladimír Hlaváč
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 837)

Abstract

Symbolic regression by the genetic programming is one of the options for obtaining a mathematical model for known data of output dependencies on inputs. Compared to neural networks (MLP), they can find a model in the form of a relatively simple mathematical relationship. The disadvantage is their computational difficulty. The following text describes several algorithm adjustments to enable acceleration and wider usage of the genetic programming. The performance of the resulting program was verified by several test functions containing several percent of the noise. The results are presented in graphs. The application is available at www.zpp.wz.cz/g.

Keywords

Symbolic regression Genetic programming Exponencionated gradient descent Constant evaluation 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Faculty of Mechanical EngineeringCzech Technical University in PraguePrague 6Czech Republic

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