GA-FreeCell: evolving solvers for the game of FreeCell

  • Authors:

  • Achiya Elyasaf;Ami Hauptman;Moshe Sipper

  • Affiliations:

  • Ben-Gurion University of the Negev, Beer Sheva, Israel;Ben-Gurion University of the Negev, Beer Sheva, Israel;Ben-Gurion University of the Negev, Beer Sheva, Israel

  • Venue:
  • Proceedings of the 13th annual conference on Genetic and evolutionary computation
  • Year:
  • 2011

Quantified Score

Hi-index 0.01

Visualization

Abstract

We evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this NP-Complete, human-challenging puzzle. We first devise several novel heuristic measures and then employ a Hillis-style coevolutionary genetic algorithm to find efficient combinations of these heuristics. Our results significantly surpass the best published solver to date by three distinct measures: 1) Number of search nodes is reduced by 87%; 2) time to solution is reduced by 93%; and 3) average solution length is reduced by 41%. Our top solver is the best published FreeCell player to date, solving 98% of the standard Microsoft 32K problem set, and also able to beat high-ranking human players.