@inproceedings{5586ce9b4312402a9e13c5ef3960945e,
title = "GA-freecell: Evolving solvers for the game of FreeCell",
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 Free-Cell player to date, solving 98% of the standard Microsoft 32K problem set, and also able to beat high-ranking human players.",
keywords = "FreeCell puzzle, Genetic algorithms, Heuristics, Hyper-heuristics, Single-agent search",
author = "Achiya Elyasaf and Ami Hauptman and Moshe Sipper",
year = "2011",
month = aug,
day = "24",
doi = "10.1145/2001576.2001836",
language = "English",
isbn = "9781450305570",
series = "Genetic and Evolutionary Computation Conference, GECCO'11",
pages = "1931--1938",
booktitle = "Genetic and Evolutionary Computation Conference, GECCO'11",
note = "13th Annual Genetic and Evolutionary Computation Conference, GECCO'11 ; Conference date: 12-07-2011 Through 16-07-2011",
}