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In computer chess or games, where the objective function reflects the [[Playing Strength|playing strength]] to maximize, SPSA can be used in [[Automated Tuning|automated tuning]] of [[Evaluation|evaluation]] parameters as well as [[Search|search]] parameters. A prominent SPSA instance is devised from [[Stockfish's Tuning Method|Stockfish's tuning method]] as introduced by [[Joona Kiiski]] in 2011 <ref>[http://www.talkchess.com/forum/viewtopic.php?start=0&t=40662 Stockfish's tuning method] by [[Joona Kiiski]], [[CCC]], October 07, 2011</ref>, where the objective function is measured once per iteration by playing a pair of games with Θ+ versus Θ-, the function "match" returning a ±2 range, see pseudo code. The selection of the coefficients A, a, c, α and γ determine the initial values and time decay of the gain sequences ak and ck, is critical to the performance of SPSA. Spall recommends using α = 0.602 and γ = 0.101, which are the lowest possible values which theoretically guarantees convergence, further see the practical suggestions in Spall's 1998 SPSA implementation paper <ref>[[James C. Spall]] ('''1998'''). ''Implementation of the Simultaneous Perturbation Algorithm for Stochastic Optimization''. [[IEEE#TOCAES|IEEE Transactions on Aerospace and Electronic Systems]], Vol. 34, No. 3, [http://www.jhuapl.edu/spsa/PDF-SPSA/Spall_Implementation_of_the_Simultaneous.PDF pdf]</ref>.