Redundancies in linear GP, canonical transformation, and its exploitation: a demonstration on image feature synthesis

  • Authors:

  • Ukrit Watchareeruetai;Yoshinori Takeuchi;Tetsuya Matsumoto;Hiroaki Kudo;Noboru Ohnishi

  • Affiliations:

  • Department of Media Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan 464-8603 and International College, King Mongkut's Institute of Technology Ladkrabang, Bangkok ...;Department of Media Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan 464-8603;Department of Media Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan 464-8603;Department of Media Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan 464-8603;Department of Media Science, Graduate School of Information Science, Nagoya University, Nagoya, Japan 464-8603

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2011

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Abstract

This paper concerns redundancies in representation of linear genetic programming (GP). We identify the causes of redundancies in linear GP and propose a canonical transformation that converts original linear representations into a canonical form in which structural redundancies are removed. In canonical form, we can easily verify whether two representations represent an identical program. We then discuss exploitation of the proposed canonical transformation, and demonstrate a way to improve search performance of linear GP by avoiding redundant individuals. Experiments were conducted with an image feature synthesis problem. Firstly, we have verified that there are really a lot of redundancies in conventional linear GP. We then investigate the effect of avoiding redundant individuals. The results yield that linear GP with avoidance of redundant individuals obviously outperforms conventional linear GP.