Visualizing Tree Structures in Genetic Programming

  • Authors:

  • Jason M. Daida;Adam M. Hilss;David J. Ward;Stephen L. Long

  • Affiliations:

  • Center for the Study of Complex Systems and Department of Atmospheric, Oceanic and Space Sciences, The University of Michigan, Ann Arbor, USA 48109-2143;Center for the Study of Complex Systems and Department of Atmospheric, Oceanic and Space Sciences, The University of Michigan, Ann Arbor, USA 48109-2143;Center for the Study of Complex Systems and Department of Atmospheric, Oceanic and Space Sciences, The University of Michigan, Ann Arbor, USA 48109-2143;Center for the Study of Complex Systems and Department of Atmospheric, Oceanic and Space Sciences, The University of Michigan, Ann Arbor, USA 48109-2143

  • Venue:
  • Genetic Programming and Evolvable Machines
  • Year:
  • 2005

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Abstract

This paper presents methods to visualize the structure of trees that occur in genetic programming. These methods allow for the inspection of structure of entire trees even though several thousands of nodes may be involved. The methods also scale to allow for the inspection of structure for entire populations and for complete trials even though millions of nodes may be involved. Examples are given that demonstrate how this new way of "seeing" can afford a potentially rich way of understanding dynamics that underpin genetic programming. The examples indicate further studies that might be enabled by visualizing structure at these scales.