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Genetic Programming and Evolvable Machines

, Volume 2, Issue 3, pp 231–257 | Cite as

Accelerated Genetic Programming of Polynomials

  • Nikolay I. Nikolaev
  • Hitoshi Iba
Article

Abstract

An accelerated polynomial construction technique for genetic programming is proposed. This is a horizontal technique for gradual expansion of a partial polynomial during traversal of its tree-structured representation. The coefficients of the partial polynomial and the coefficient of the new term are calculated by a rapid recurrent least squares (RLS) fitting method. When used for genetic programming (GP) of polynomials this technique enables us not only to achieve fast estimation of the coefficients, but also leads to power series models that differ from those of traditional Koza-style GP and from those of the previous GP with polynomials STROGANOFF. We demonstrate that the accelerated GP is sucessful in that it evolves solutions with greater generalization capacity than STROGANOFF and traditional GP on symbolic regression, pattern recognition, and financial time-series prediction tasks.

Keywords

Operating System Artificial Intelligence Pattern Recognition Power Series Genetic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • Nikolay I. Nikolaev
    • 1
  • Hitoshi Iba
    • 2
  1. 1.Department of Mathematics & Computing Sciences, Goldsmiths CollegeUniversity of London, New CrossLondonUnited Kingdom
  2. 2.Department of Information and Communication Engineering, School of EngineeringThe University of TokyoTokyoJapan

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