Evolutionary design of freecell solvers

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


In this paper, we evolve heuristics to guide staged deepening search for the hard game of FreeCell, obtaining top-notch solvers for this human-challenging puzzle. We first devise several novel heuristic measures using minimal domain knowledge and then use them as building blocks in two evolutionary setups involving a standard genetic algorithm and policy-based, genetic programming. Our evolved solvers outperform the best FreeCell solver to date by three distinct measures: 1) number of search nodes is reduced by over 78%; 2) time to solution is reduced by over 94%; and 3) average solution length is reduced by over 30%. Our top solver is the best published FreeCell player to date, solving 99.65% of the standard Microsoft 32 K problem set. Moreover, it is able to convincingly beat high-ranking human players.

Original languageEnglish
Article number6249736
Pages (from-to)270-281
Number of pages12
JournalIEEE Transactions on Computational Intelligence and AI in Games
Issue number4
StatePublished - 26 Dec 2012


  • Evolutionary algorithms
  • FreeCell
  • genetic algorithms (GAs)
  • genetic programing (GP)
  • heuristic
  • hyperheuristic

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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