Accelerated Genetic Programming of Polynomials

  • Authors:

  • Nikolay I. Nikolaev;Hitoshi Iba

  • Affiliations:

  • Department of Mathematics & Computing Sciences, Goldsmiths College, University of London, New Cross, London SE14 6NW, United Kingdom nikolaev@mcs.gold.ac.uk;Department of Information and Communication Engineering, School of Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan iba@miv.t.u-tokyo.ac.jp

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2001

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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.