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Evolutionary Design of FreeCell Solvers

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3 Author(s)
Achiya Elyasaf ; Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel ; Ami Hauptman ; Moshe Sipper

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.

Published in:

IEEE Transactions on Computational Intelligence and AI in Games  (Volume:4 ,  Issue: 4 )