What Makes a Problem GP-Hard? Analysis of a Tunably Difficult Problem in Genetic Programming

  • Authors:

  • Jason M. Daida;Robert R. Bertram;Stephen A. Stanhope;Jonathan C. Khoo;Shahbaz A. Chaudhary;Omer A. Chaudhri;John A. Ii Polito

  • Affiliations:

  • The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;The University of Michigan, Artificial Intelligence Laboratory and Space Physics Research Laboratory, 2455 Hayward Avenue, Ann Arbor, MI 48109-2143 USA;Consilient, Inc., 1815 4th Street, Suite B, Berkeley, CA 94710 USA

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

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Abstract

This paper addresses the issue of what makes a problem genetic programming (GP)-hard by considering the binomial-3 problem. In the process, we discuss the efficacy of the metaphor of an adaptive fitness landscape to explain what is GP-hard. We indicate that, at least for this problem, the metaphor is misleading.