GP-rush: using genetic programming to evolve solvers for the rush hour puzzle

  • Authors:

  • Ami Hauptman;Achiya Elyasaf;Moshe Sipper;Assaf Karmon

  • Affiliations:

  • Ben-Gurion University, Be'er-Sheva, Israel;Ben-Gurion University, Be'er-Sheva, Israel;Ben-Gurion University, Be'er-Sheva, Israel;Ben-Gurion University, Be'er-Sheva, Israel

  • Venue:
  • Proceedings of the 11th Annual conference on Genetic and evolutionary computation
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

We evolve heuristics to guide IDA* search for the 6x6 and 8x8 versions of the Rush Hour puzzle, a PSPACE-Complete problem, for which no efficient solver has yet been reported. No effective heuristic functions are known for this domain, and--before applying any evolutionary thinking--we first devise several novel heuristic measures, which improve (non-evolutionary) search for some instances, but hinder search substantially for many other instances. We then turn to genetic programming (GP) and find that evolution proves immensely efficacious, managing to combine heuristics of such highly variable utility into composites that are nearly always beneficial, and far better than each separate component. GP is thus able to beat both the human player of the game and also the human designers of heuristics.