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Automated Tuning

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* [[Amoeba]]
* [[BBChess (SI)#DifferentialEvolution|Differential Evolution in BBChess]]
* [[Deuterium]]
* [[Falcon#GA|Genetic Algorithm in Falcon]]
* [[Stockfish's Tuning Method]]
* [https://en.wikipedia.org/wiki/Time_complexity Time complexity] issues with increasing number of weights to tune
<span id="ReinformentLearning"></span>
=Reinforment Reinforcement Learning=[[Reinforcement Learning|Reinforcement learning]], in particular [[Temporal Difference Learning|temporal difference learning]], has a long history in tuning evaluation weights in game programming, first seeen in the late 50s by [[Arthur Samuel]] in his [[Checkers]] player <ref>[[Arthur Samuel]] ('''1959'''). ''[http://domino.watson.ibm.com/tchjr/journalindex.nsf/600cc5649e2871db852568150060213c/39a870213169f45685256bfa00683d74!OpenDocument Some Studies in Machine Learning Using the Game of Checkers]''. IBM Journal July 1959</ref>. In self play against a stable copy of itself, after each move, the weights of the evaluation function were adjusted in a way that the [[Score|score]] of the [[Root|root position]] after a [[Quiescence Search|quiescence search]] became closer to the score of the full search. This TD method was generalized and formalized by [[Richard Sutton]] in 1988 <ref>[[Richard Sutton]] ('''1988'''). ''Learning to Predict by the Methods of Temporal Differences''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Vol. 3, No. 1, [http://webdocs.cs.ualberta.ca/~sutton/papers/sutton-88.pdf pdf]</ref>, who introduced the decay parameter '''λ''', where proportions of the score came from the outcome of [https://en.wikipedia.org/wiki/Monte_Carlo_method Monte Carlo] simulated games, tapering between [https://en.wikipedia.org/wiki/Bootstrapping#Artificial_intelligence_and_machine_learning bootstrapping] (λ = 0) and Monte Carlo (λ = 1). [[Temporal Difference Learning#TDLamba|TD-λ]] was famously applied by [[Gerald Tesauro]] in his [[Backgammon]] program [https://en.wikipedia.org/wiki/TD-Gammon TD-Gammon] <ref>[[Gerald Tesauro]] ('''1992'''). ''Temporal Difference Learning of Backgammon Strategy''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/ml1992.html#Tesauro92 ML 1992]</ref> <ref>[[Gerald Tesauro]] ('''1994'''). ''TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play''. [http://www.informatik.uni-trier.de/~ley/db/journals/neco/neco6.html#Tesauro94 Neural Computation Vol. 6, No. 2]</ref>, its [[Minimax|minimax]] adaption adaptation [[Temporal Difference Learning#TDLeaf|TD-Leaf]] was successful used in eval tuning of chess programs <ref>[[Don Beal]], [[Martin C. Smith]] ('''1999'''). ''Learning Piece-Square Values using Temporal Differences.'' [[ICGA Journal#22_4|ICCA Journal, Vol. 22, No. 4]]</ref>, with [[KnightCap]] <ref>[[Jonathan Baxter]], [[Andrew Tridgell]], [[Lex Weaver]] ('''1998'''). ''Experiments in Parameter Learning Using Temporal Differences''. [[ICGA Journal#21_2|ICCA Journal, Vol. 21, No. 2]], [http://cs.anu.edu.au/%7ELex.Weaver/pub_sem/publications/ICCA-98_equiv.pdf pdf]</ref> and [[CilkChess]] <ref>[http://supertech.csail.mit.edu/chess/ The Cilkchess Parallel Chess Program]</ref> as prominent samples.
==Instances==
<span id="SupervisedLearning"></span>
=Supervised Learning=
==Move AdaptionAdaptation==<span id="MoveAdaption"></span>One [[Supervised Learning|supervised learning]] method considers desired moves from a set of positions, likely from grandmaster games, and tries to adjust their evaluation weights so that for instance a one-ply search agrees with the desired move. Already pioneering in reinforcement learning some years before, move adaption adaptation was described by [[Arthur Samuel]] in 1967 as used in the second version of his checkers player <ref>[[Arthur Samuel]] ('''1967'''). ''Some Studies in Machine Learning. Using the Game of Checkers. II-Recent Progress''. [http://researcher.watson.ibm.com/researcher/files/us-beygel/samuel-checkers.pdf pdf]</ref>, where a structure of stacked linear evaluation functions was trained by computing a correlation measure based on the number of times the feature rated an alternative move higher than the desired move played by an expert <ref>[[Johannes Fürnkranz]] ('''2000'''). ''Machine Learning in Games: A Survey''. [https://en.wikipedia.org/wiki/Austrian_Research_Institute_for_Artificial_Intelligence Austrian Research Institute for Artificial Intelligence], OEFAI-TR-2000-3, [http://www.ofai.at/cgi-bin/get-tr?download=1&paper=oefai-tr-2000-31.pdf pdf]</ref>. In chess, move adaption adaptation was first described by [[Thomas Nitsche]] in 1982 <ref>[[Thomas Nitsche]] ('''1982'''). ''A Learning Chess Program.'' [[Advances in Computer Chess 3]]</ref>, and with some extensions by [[Tony Marsland]] in 1985 <ref>[[Tony Marsland]] ('''1985'''). ''Evaluation-Function Factors''. [[ICGA Journal#8_2|ICCA Journal, Vol. 8, No. 2]], [http://webdocs.cs.ualberta.ca/~tony/OldPapers/evaluation.pdf pdf]</ref>. [[Eval Tuning in Deep Thought]] as mentioned by [[Feng-hsiung Hsu]] et al. in 1990 <ref>[[Feng-hsiung Hsu]], [[Thomas Anantharaman]], [[Murray Campbell]], [[Andreas Nowatzyk]] ('''1990'''). ''[http://www.disi.unige.it/person/DelzannoG/AI2/hsu.html A Grandmaster Chess Machine]''. [[Scientific American]], Vol. 263, No. 4, pp. 44-50. ISSN 0036-8733.</ref>, and later published by [[Andreas Nowatzyk]], is also based on an extended form of move adaption adaptation <ref>see ''2.1 Learning from Desired Moves in Chess'' in [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2014'''). ''[https://www.jair.org/papers/paper4217.html Large-Scale Optimization for Evaluation Functions with Minimax Search]''. [https://www.jair.org/vol/vol49.html JAIR Vol. 49]</ref>. [[Jonathan Schaeffer|Jonathan Schaeffer's]] and [[Paul Lu|Paul Lu's]] efforts to make Deep Thought's approach work for [https://en.wikipedia.org/wiki/Chinook_%28draughts_player%29 Chinook] in 1990 failed <ref>[[Jonathan Schaeffer]], [[Joe Culberson]], [[Norman Treloar]], [[Brent Knight]], [[Paul Lu]], [[Duane Szafron]] ('''1992'''). ''A World Championship Caliber Checkers Program''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 53, Nos. 2-3,[http://webdocs.cs.ualberta.ca/%7Ejonathan/Papers/Papers/chinook.ps ps]</ref> - nothing seemed to produce results that were as good than their hand-tuned effort <ref>[[Jonathan Schaeffer]] ('''1997, 2009'''). ''[http://www.springer.com/computer/ai/book/978-0-387-76575-4 One Jump Ahead]''. 7. The Case for the Prosecution, pp. 111-114</ref>.
==Value AdaptionAdaptation ==<span id="ValueAdaption"></span>A second supervised learning approach used to tune evaluation weights is based on [https://en.wikipedia.org/wiki/Regression regression] of the desired value, i.e. using the final outcome from huge sets of positions from quality games, or other information supplied by a supervisor, i.e. in form of annotations from [https://en.wikipedia.org/wiki/Chess_annotation_symbols#Position_evaluation_symbols position evaluation symbols]. Often, value adaption adaptation is reinforced by determining an expected outcome by self play <ref>[[Bruce Abramson]] ('''1990'''). ''[http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=44404 Expected-Outcome: A General Model of Static Evaluation]''. [[IEEE#TPAMI|IEEE Transactions on Pattern Analysis and Machine Intelligence]], Vol. 12, No. 2</ref>.
==Advantages==
<span id="Regression"></span>
=Regression=
[[FILE:Linear regression.svg|border|right|thumb|300px|[https://en.wikipedia.org/wiki/Linear_regression Linear Regression] <ref>Random data points and their [https://en.wikipedia.org/wiki/Linear_regression linear regression]. [https://commons.wikimedia.org/wiki/File:Linear_regression.svg Created] with [https://en.wikipedia.org/wiki/Sage_%28mathematics_software%29 Sage] by Sewaqu, November 5, 2010, [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref> ]]
 
[https://en.wikipedia.org/wiki/Regression_analysis Regression analysis] is a [https://en.wikipedia.org/wiki/Statistics statistical process] with a substantial overlap with machine learning to [https://en.wikipedia.org/wiki/Prediction predict] the value of an [https://en.wikipedia.org/wiki/Dependent_and_independent_variables Y variable] (output), given known value pairs of the X and Y variables. While [https://en.wikipedia.org/wiki/Linear_regression linear regression] deals with continuous outputs, [https://en.wikipedia.org/wiki/Logistic_regression logistic regression] covers binary or discrete output, such as win/loss, or win/draw/loss. Parameter estimation in regression analysis can be formulated as the [https://en.wikipedia.org/wiki/Mathematical_optimization minimization] of a [https://en.wikipedia.org/wiki/Loss_function cost or loss function] over a [https://en.wikipedia.org/wiki/Training_set training set] <ref>[https://en.wikipedia.org/wiki/Loss_function#Use_in_statistics Loss function - Use in statistics - Wkipedia]</ref>, such as [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] or [https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression cross-entropy error function] for [https://en.wikipedia.org/wiki/Binary_classification binary classification] <ref>"Using [https://en.wikipedia.org/wiki/Cross_entropy#Cross-entropy_error_function_and_logistic_regression cross-entropy error function] instead of [https://en.wikipedia.org/wiki/Mean_squared_error sum of squares] leads to faster training and improved generalization", from [https://en.wikipedia.org/wiki/Sargur_Srihari Sargur Srihari], [http://www.cedar.buffalo.edu/~srihari/CSE574/Chap5/Chap5.2-Training.pdf Neural Network Training] (pdf)</ref>. The minimization is implemented by [[Iteration|iterative]] optimization [[Algorithms|algorithms]] or [https://en.wikipedia.org/wiki/Metaheuristic metaheuristics] such as [https://en.wikipedia.org/wiki/Iterated_local_search Iterated local search], [https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm Gauss–Newton algorithm], or [https://en.wikipedia.org/wiki/Conjugate_gradient_method conjugate gradient method].
<span id="LinearRegression"></span>
==Linear Regression==
{||-| style="vertical-align:top;" | The supervised problem of regression applied to [[Automated Tuning#MoveAdaption|move adaptionadaptation]] was used by [[Thomas Nitsche]] in 1982, minimizing the [https://en.wikipedia.org/wiki/Mean_squared_error mean squared error] of a cost function considering the program’s and a grandmaster’s choice of moves, as mentioned, extended by [[Tony Marsland]] in 1985, and later by the [[Deep Thought]] team. Regression used to [[Automated Tuning#ValueAdaption|adapt desired values]] was described by [[Donald H. Mitchell]] in his 1984 masters master thesis on evaluation features in [[Othello]], cited by [[Michael Buro]] <ref>[[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/jair/article/view/paper179.html 10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3</ref> <ref>[[Donald H. Mitchell]] ('''1984'''). ''Using Features to Evaluate Positions in Experts' and Novices' Othello Games''. Masters Master thesis, Department of Psychology, [[Northwestern University]], Evanston, IL</ref>. [[Jens Christensen]] applied [https://en.wikipedia.org/wiki/Linear_regression linear regression] to chess in 1986 to learn [[Point Value|point values]] in the domain of [[Temporal Difference Learning|temporal difference learning]] <ref>[[Jens Christensen]] ('''1986'''). ''[http://link.springer.com/chapter/10.1007/978-1-4613-2279-5_9?no-access=true Learning Static Evaluation Functions by Linear Regression]''. in [[Tom Mitchell]], [[Jaime Carbonell]], [[Ryszard Michalski]] ('''1986'''). ''[http://link.springer.com/book/10.1007/978-1-4613-2279-5 Machine Learning: A Guide to Current Research]''. The Kluwer International Series in Engineering and Computer Science, Vol. 12</ref>. | [[FILE:Linear regression.svg|border|left|thumb|baseline|300px|[https://en.wikipedia.org/wiki/Linear_regression Linear Regression] <ref>Random data points and their [https://en.wikipedia.org/wiki/Linear_regression linear regression]. [https://commons.wikimedia.org/wiki/File:Linear_regression.svg Created] with [https://en.wikipedia.org/wiki/Sage_%28mathematics_software%29 Sage] by Sewaqu, November 5, 2010, [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref> ]] |}
<span id="LogisticRegression"></span>
==Logistic Regression==
{ [[FILE:SigmoidTexelTune.gif|border|right|thumb|300px|link=http://wolfr.am/1al3d5B|[https://en.wikipedia.org/wiki/Logistic_function Logistic function] <ref>[http://wolfr.am/1al3d5B log-| stylelinear 1 / (1 + 10^(-s/4)) , s="vertical-align10 to 10] from [https:top;" //en.wikipedia.org/wiki/Wolfram_Alpha Wolfram| Alpha]</ref> ]]  Since the relationship between [[Pawn Advantage, Win Percentage, and Elo|win percentage and pawn advantage]] is assumed to follow a [https://en.wikipedia.org/wiki/Logistic_model logistic model], one may treat static evaluation as [[Neural Networks#Perceptron|single-layer perceptron]] or single [https://en.wikipedia.org/wiki/Artificial_neuron neuron] [[Neural Networks|ANN]] with the common [https://en.wikipedia.org/wiki/Logistic_function logistic] [https://en.wikipedia.org/wiki/Activation_function activation function], performing the perceptron algorithm to train it <ref>[http://www.talkchess.com/forum/viewtopic.php?t=56168&start=36 Re: Piece weights with regression analysis (in Russian)] by [[Fabien Letouzey]], [[CCC]], May 04, 2015</ref>. [https://en.wikipedia.org/wiki/Logistic_regression Logistic regression] in evaluation tuning was first elaborated by [[Michael Buro]] in 1995 <ref>[[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/paper179.html jair/article/view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3</ref>, and proved successful in the game of [[Othello]] in comparison with [[Mathematician#RFisher|Fisher's]] [https://en.wikipedia.org/wiki/Kernel_Fisher_discriminant_analysis linear discriminant] and quadratic [https://en.wikipedia.org/wiki/Discriminant discriminant] function for [https://en.wikipedia.org/wiki/Normal_distribution normally distributed] features, and served as eponym of his Othello program ''Logistello'' <ref>[https://skatgame.net/mburo/log.html LOGISTELLO's Homepage]</ref>. In computer chess, logistic regression was applied by [[Arkadiusz Paterek]] with [[Gosu]] <ref>[[Arkadiusz Paterek]] ('''2004'''). ''Modelowanie funkcji oceniającej w grach''. [[University of Warsaw]], [https://www.mimuw.edu.pl/~paterek/mfog.ps.gz zipped ps] (Polish, Modeling of an evaluation function in games)</ref>, later proposed by [[Miguel A. Ballicora]] in 2009 as used by [[Gaviota]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=27266&postdays=0&postorder=asc&topic_view=&start=11 Re: Insanity... or Tal style?] by [[Miguel A. Ballicora]], [[CCC]], April 02, 2009</ref>, independently described by [[Amir Ban]] in 2012 for [[Junior|Junior's]] evaluation learning <ref>[[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]</ref>, and explicitly mentioned by [[Álvaro Begué]] in a January 2014 [[CCC]] discussion <ref>[http://www.talkchess.com/forum/viewtopic.php?t=50823&start=10 Re: How Do You Automatically Tune Your Evaluation Tables] by [[Álvaro Begué]], [[CCC]], January 08, 2014</ref>, when [[Peter Österlund]] explained [[Texel's Tuning Method]] <ref>[http://www.talkchess.com/forum/viewtopic.php?topic_view=threads&p=555522&t=50823 The texel evaluation function optimization algorithm] by [[Peter Österlund]], [[CCC]], January 31, 2014</ref>, which subsequently popularized logistic regression tuning in computer chess. [[Vladimir Medvedev|Vladimir Medvedev's]] [[Point Value by Regression Analysis]] <ref>[http://habrahabr.ru/post/254753/ Определяем веса шахматных фигур регрессионным анализом / Хабрахабр] by [[Vladimir Medvedev|WinPooh]], April 27, 2015 (Russian)</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=56168 Piece weights with regression analysis (in Russian)] by [[Vladimir Medvedev]], [[CCC]], April 30, 2015</ref> experiments showed why the [https://en.wikipedia.org/wiki/Logistic_function logistic function] is appropriate, and further used [https://en.wikipedia.org/wiki/Cross_entropy cross-entropy] and [https://en.wikipedia.org/wiki/Regularization_%28mathematics%29 regularization].| [[FILE:SigmoidTexelTune.gif|border|left|thumb|baseline|300px|link=http://wolfr.am/1al3d5B|[https://en.wikipedia.org/wiki/Logistic_function Logistic function] <ref>[http://wolfr.am/1al3d5B log-linear 1 / (1 + 10^(-s/4)) , s=-10 to 10] from [https://en.wikipedia.org/wiki/Wolfram_Alpha Wolfram|Alpha]</ref> ]] |}
==Instances==
* [[Arasan#Tuning|Arasan's Tuning]]
* [[Ethereal]]
* [[Eval Tuning in Deep Thought]]
* [[FabChess]]
* [[Gosu]]
* [[Koivisto]]
* [[Minimax Tree Optimization]] (MMTO or the Bonanza-Method in [[Shogi]])
* [[Point Value by Regression Analysis]]
==1970 ...==
* [[Arnold K. Griffith]] ('''1974'''). ''[http://www.sciencedirect.com/science/article/pii/0004370274900277 A Comparison and Evaluation of Three Machine Learning Procedures as Applied to the Game of Checkers]''. [https://en.wikipedia.org/wiki/Artificial_Intelligence_%28journal%29 Artificial Intelligence], Vol. 5, No. 2
* [[Mathematician#MSBazaraa|Mokhtar S. Bazaraa]], [[Mathematician#MCShetty|C. M. Shetty]] ('''1976'''). ''[https://link.springer.com/book/10.1007%2F978-3-642-48294-6 Foundations of Optimization]''. Lecture Notes in Economics and Mathematical Systems, Vol. 122, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
* <span id="NonlinearProgramming1st"></span>[[Mathematician#MSBazaraa|Mokhtar S. Bazaraa]], [[Mathematician#MCShetty|C. M. Shetty]] ('''1979'''). ''Nonlinear Programming: Theory and Algorithms''. [https://en.wikipedia.org/wiki/Wiley_(publisher) Wiley] » [[#NonlinearProgramming2nd|2nd]], [[#NonlinearProgramming3rd|3rd edition]]
==1980 ...==
* [[Thomas Nitsche]] ('''1982'''). ''A Learning Chess Program.'' [[Advances in Computer Chess 3]]
* [[Donald H. Mitchell]] ('''1984'''). ''Using Features to Evaluate Positions in Experts' and Novices' Othello Games''. Masters Master thesis, Department of Psychology, [[Northwestern University]], Evanston, IL
==1985 ...==
* [[Tony Marsland]] ('''1985'''). ''Evaluation-Function Factors''. [[ICGA Journal#8_2|ICCA Journal, Vol. 8, No. 2]], [http://webdocs.cs.ualberta.ca/~tony/OldPapers/evaluation.pdf pdf]
* [[Paul E. Utgoff]], [http://dblp.uni-trier.de/pers/hd/c/Clouse:Jeffery_A= Jeffery A. Clouse] ('''1991'''). ''[http://scholarworks.umass.edu/cs_faculty_pubs/193/ Two Kinds of Training Information for Evaluation Function Learning]''. [https://en.wikipedia.org/wiki/University_of_Massachusetts_Amherst University of Massachusetts, Amherst], Proceedings of the AAAI 1991
* [[Gerald Tesauro]] ('''1992'''). ''Temporal Difference Learning of Backgammon Strategy''. [http://www.informatik.uni-trier.de/~ley/db/conf/icml/ml1992.html#Tesauro92 ML 1992]
* [[Ingo Althöfer]] ('''1993'''). ''On Telescoping Linear Evaluation Functions.'' [[ICGA Journal#16_2|ICCA Journal, Vol. 16, No. 2]]* <span id="NonlinearProgramming2nd"></span>[[Mathematician#MSBazaraa|Mokhtar S. Bazaraa]], [[Mathematician#HDSherali|Hanif D. Sherali]], [[Mathematician#MCShetty|C. M. Shetty]] ('''1993'''). ''Nonlinear Programming: Theory and Algorithms''. 2nd edition, pp[https://en. 91-94wikipedia.org/wiki/Wiley_(publisher) Wiley] » [[#NonlinearProgramming1st|1st]], [[#NonlinearProgramming3rd|3rd edition]]
* [[Peter Mysliwietz]] ('''1994'''). ''Konstruktion und Optimierung von Bewertungsfunktionen beim Schach.'' Ph.D. thesis (German)
==1995 ...==
* [[Michael Buro]] ('''1995'''). ''[httphttps://www.jair.org/papersindex.php/jair/article/paper179.html view/10146 Statistical Feature Combination for the Evaluation of Game Positions]''. [https://en.wikipedia.org/wiki/Journal_of_Artificial_Intelligence_Research JAIR], Vol. 3
* [[Chris McConnell]] ('''1995'''). ''Tuning Evaluation Functions for Search''. [http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=9B2A0CCA8B1AFB594A879799D974111A?doi=10.1.1.53.9742&rep=rep1&type=pdf pdf]
* [[Chris McConnell]] ('''1995'''). ''Tuning Evaluation Functions for Search'' (Talk), [http://www.cs.cmu.edu/afs/cs.cmu.edu/user/ccm/www/talks/tune.ps ps]
* [[Levente Kocsis]], [[Csaba Szepesvári]], [[Mark Winands]] ('''2005'''). ''[http://link.springer.com/chapter/10.1007/11922155_4 RSPSA: Enhanced Parameter Optimization in Games]''. [[Advances in Computer Games 11]], [http://www.sztaki.hu/~szcsaba/papers/rspsa_acg.pdf pdf]
'''2006'''
* <span id="NonlinearProgramming3rd"></span>[[Mathematician#MSBazaraa|Mokhtar S. Bazaraa]], [[Mathematician#HDSherali|Hanif D. Sherali]], [[Mathematician#MCShetty|C. M. Shetty]] ('''2006'''). ''[https://www.wiley.com/en-us/Nonlinear+Programming%3A+Theory+and+Algorithms%2C+3rd+Edition-p-9780471486008 Nonlinear Programming: Theory and Algorithms]''. 3rd edition, [https://en.wikipedia.org/wiki/Wiley_(publisher) Wiley] <ref>[http://www.open-aurec.com/wbforum/viewtopic.php?f=4&t=49450&start=3 Re: Adjusting weights the Deep Blue way] by [[Pradu Kannan]], [[Computer Chess Forums|Winboard Forum]], September 01, 2008</ref> » [[#NonlinearProgramming1st|1st]], [[#NonlinearProgramming2nd|2nd edition]]
* [[Levente Kocsis]], [[Csaba Szepesvári]] ('''2006'''). ''[http://link.springer.com/article/10.1007/s10994-006-6888-8 Universal Parameter Optimisation in Games Based on SPSA]''. [https://en.wikipedia.org/wiki/Machine_Learning_%28journal%29 Machine Learning], Special Issue on Machine Learning and Games, Vol. 63, No. 3
* [[Hallam Nasreddine]], [[Hendra Suhanto Poh]], [[Graham Kendall]] ('''2006'''). ''Using an Evolutionary Algorithm for the Tuning of a Chess Evaluation Function Based on a Dynamic Boundary Strategy''. Proceedings of the 2006 [[IEEE]] Conference on Cybernetics and Intelligent Systems, [http://www.graham-kendall.com/papers/npk2006.pdf pdf]
* [[Makoto Miwa]], [[Daisaku Yokoyama]], [[Takashi Chikayama]] ('''2007'''). ''Automatic Generation of Evaluation Features for Computer Game Players''. [http://cswww.essex.ac.uk/cig/2007/papers/2037.pdf pdf]
* [[Johannes Fürnkranz]] ('''2007'''). ''Recent advances in machine learning and game playing''. [http://www.oegai.at/journal.shtml ÖGAI Journal], Vol. 26, No. 2, Computer Game Playing, [https://www.ke.tu-darmstadt.de/~juffi/publications/ogai-07.pdf pdf]
* [https://dblp.org/pid/70/382.html Mohammed Shahid Abdulla], [[Shalabh Bhatnagar]] ('''2007'''). ''[https://link.springer.com/article/10.1007/s10626-006-0003-y Reinforcement Learning Based Algorithms for Average Cost Markov Decision Processes]''. [https://www.springer.com/journal/10626 Discrete Event Dynamic Systems], Vol.17, No.1 » [[SPSA]]
'''2008'''
* [[Eli David|Omid David]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2008'''). ''Genetic Algorithms for Mentor-Assisted Evaluation Function Optimization''. [http://www.sigevo.org/gecco-2008/ GECCO '08], [https://arxiv.org/abs/1711.06839 arXiv:1711.06839]
* [[Borko Bošković]], [[Sašo Greiner]], [[Janez Brest]], [[Aleš Zamuda]], [[Viljem Žumer]] ('''2008'''). ''[https://link.springer.com/chapter/10.1007%2F978-3-540-68830-3_12 An Adaptive Differential Evolution Algorithm with Opposition-Based Mechanisms, Applied to the Tuning of a Chess Program]''. [https://link.springer.com/book/10.1007/978-3-540-68830-3 Advances in Differential Evolution], [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer]
'''2009'''
* [[Joel Veness]], [[David Silver]], [[William Uther]], [[Alan Blair]] ('''2009'''). ''[http://papers.nips.cc/paper/3722-bootstrapping-from-game-tree-search Bootstrapping from Game Tree Search]''. [http://nips.cc/ Neural Information Processing Systems (NIPS), 2009], [http://booksjveness.nipsinfo/publications/nips2009%20-%20bootstrapping%20from%20game%20tree%20search.ccpdf pdf] » [[Meep]] <ref>[http:/papers/fileswww.talkchess.com/nips22forum/NIPS2009_0508viewtopic.pdf pdfphp?start=0&t=31667 A paper about parameter tuning] by [[Rémi Coulom]], [[CCC]], January 12, 2010</ref>
* [[Eli David|Omid David]], [[Jaap van den Herik]], [[Moshe Koppel]], [[Nathan S. Netanyahu]] ('''2009'''). ''Simulating Human Grandmasters: Evolution and Coevolution of Evaluation Functions''. [http://www.sigevo.org/gecco-2009/ GECCO '09], [https://arxiv.org/abs/1711.06840 arXiv:1711.0684]
* [[Eli David|Omid David]] ('''2009'''). ''Genetic Algorithms Based Learning for Evolving Intelligent Organisms''. Ph.D. Thesis.
* [[Rémi Coulom]] ('''2011'''). ''[http://remi.coulom.free.fr/CLOP/ CLOP: Confident Local Optimization for Noisy Black-Box Parameter Tuning]''. [[Advances in Computer Games 13]] <ref>[http://www.talkchess.com/forum/viewtopic.php?p=421995 CLOP for Noisy Black-Box Parameter Optimization] by [[Rémi Coulom]], [[CCC]], September 01, 2011</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=40987 CLOP slides] by [[Rémi Coulom]], [[CCC]], November 03, 2011</ref>
* [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2011'''). ''[http://link.springer.com/chapter/10.1007%2F978-3-642-31866-5_16 The Global Landscape of Objective Functions for the Optimization of Shogi Piece Values with a Game-Tree Search]''. [[Advances in Computer Games 13]] » [[Shogi]]
* [[Mathematician#JCDuchi|John Duchi]], [[Mathematician#EHazan|Elad Hazan]], [[Mathematician#YSinger|Yoram Singer]] ('''2011'''). ''[https://dl.acm.org/doi/10.5555/1953048.2021068 Adaptive Subgradient Methods for Online Learning and Stochastic Optimization]''. [https://en.wikipedia.org/wiki/Journal_of_Machine_Learning_Research Journal of Machine Learning Research], Vol. 12, [https://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf pdf] » [https://en.wikipedia.org/wiki/Stochastic_gradient_descent#AdaGrad AdaGrad]
'''2012'''
* [[Amir Ban]] ('''2012'''). ''[http://www.ratio.huji.ac.il/node/2362 Automatic Learning of Evaluation, with Applications to Computer Chess]''. Discussion Paper 613, [https://en.wikipedia.org/wiki/Hebrew_University_of_Jerusalem The Hebrew University of Jerusalem] - Center for the Study of Rationality, [https://en.wikipedia.org/wiki/Givat_Ram Givat Ram]
* [[Thitipong Kanjanapa]], [[Kanako Komiya]], [[Yoshiyuki Kotani]] ('''2012'''). ''Design and Implementation of Bonanza Method for the Evaluation in the Game of Arimaa''. [http://www.ipsj.or.jp/english/index.html IPSJ SIG Technical Report], Vol. 2012-GI-27, No. 4, [http://arimaa.com/arimaa/papers/KanjanapaThitipong/IPSJ-GI12027004.pdf pdf] » [[Arimaa]]
* [[Alan J. Lockett]] ('''2012'''). ''General-Purpose Optimization Through Information Maximization''. Ph.D. thesis, [https://en.wikipedia.org/wiki/University_of_Texas_at_Austin University of Texas at Austin], advisor [[Risto Miikkulainen]], [http://www.alockett.com/static/pdf/lockett-thesis.pdf pdf]
'''2013'''
* [[Alan J. Lockett]], [[Risto Miikkulainen]] ('''2013'''). ''[http://nn.cs.utexas.edu/?lockett:foga2013 A Measure-Theoretic Analysis of Stochastic Optimization]''. [https://dblp.uni-trier.de/db/conf/foga/foga2013.html FOGA 2013]
* [[Wen-Jie Tseng]], [[Jr-Chang Chen]], [[I-Chen Wu]], [[Ching-Hua Kuo]], [[Bo-Han Lin]] ('''2013'''). ''[https://kaigi.org/jsai/webprogram/2013/paper-138.html A Supervised Learning Method for Chinese Chess Programs]''. [http://2013.conf.ai-gakkai.or.jp/english-info JSAI2013], [https://kaigi.org/jsai/webprogram/2013/pdf/138.pdf pdf]
* [[Akira Ura]], [[Makoto Miwa]], [[Yoshimasa Tsuruoka]], [[Takashi Chikayama]] ('''2013'''). ''[https://link.springer.com/chapter/10.1007/978-3-319-09165-5_18 Comparison Training of Shogi Evaluation Functions with Self-Generated Training Positions and Moves]''. [[CG 2013]], [https://pdfs.semanticscholar.org/6ad0/7167425539cf64e6bf420d7a28a1fc1047d6.pdf slides as pdf]
* [[Yoshikuni Sato]], [[Makoto Miwa]], [[Shogo Takeuchi]], [[Daisuke Takahashi]] ('''2013'''). ''[http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6402 Optimizing Objective Function Parameters for Strength in Computer Game-Playing]''. [http://www.informatik.uni-trier.de/~ley/db/conf/aaai/aaai2013.html#SatoMTT13 AAAI 2013]
* [[Shalabh Bhatnagar]], [[https://dblp.org/pid/31/10493.html H. L. Prasad]], [[https://scholar.google.co.in/citations?user=Q1YXWpoAAAAJ&hl=en L.A. Prashanth]] ('''2013'''). ''[httphttps://stochastic.csa.iisclink.ernetspringer.incom/~shalabhbook/book10.html 1007/978-1-4471-4285-0 Stochastic Recursive Algorithms for Optimization: Simultaneous Perturbation Methods]''. [httphttps://www.springer.com/series/642 Lecture Notes in Control and Information Sciences], Vol. 434, [https://en.wikipedia.org/wiki/Springer_Science%2BBusiness_Media Springer] » [[SPSA]]
* [[Tomáš Hřebejk]] ('''2013'''). ''Arimaa challenge - Static Evaluation Function''. Master Thesis, [https://en.wikipedia.org/wiki/Charles_University_in_Prague Charles University in Prague], [http://arimaa.com/arimaa/papers/ThomasHrebejk/Arimaa.pdf pdf] » [[Arimaa]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=58472 thesis on eval function learning in Arimaa] by [[Jon Dart]], [[CCC]], December 04, 2015</ref>
* [[Yoshikuni Sato]], [[Makoto Miwa]], [[Shogo Takeuchi]], [[Daisuke Takahashi]] ('''2013'''). ''[http://www.aaai.org/ocs/index.php/AAAI/AAAI13/paper/view/6402 Optimizing Objective Function Parameters for Strength in Computer Game-Playing]''. [http://www.informatik.uni-trier.de/~ley/db/conf/aaai/aaai2013.html#SatoMTT13 AAAI 2013]
* [[Ilya Loshchilov]] ('''2013'''). ''[http://loshchilov.com/phd.html Surrogate-Assisted Evolutionary Algorithms]''. Ph.D. thesis, [[University of Paris#11|Paris-Sud 11 University]], advisors [[Marc Schoenauer]] and [[Michèle Sebag]]
* [https://www.cs.ubc.ca/~schmidtm/ Mark Schmidt], [https://inria.academia.edu/NicolasLeRoux Nicolas Le Roux], [https://www.di.ens.fr/~fbach/ Francis Bach] ('''2013'''). ''Minimizing Finite Sums with the Stochastic Average Gradient''. [https://arxiv.org/abs/1309.2388 arXiv:1309.2388] <ref>[https://groups.google.com/d/msg/fishcooking/XnLmUP_78iw/QgMZzmeVBgAJ Tuning floats] by [[Stephane Nicolet]], [[Computer Chess Forums|FishCooking]], April 12, 2018</ref>
'''2014'''
* [[Kunihito Hoki]], [[Tomoyuki Kaneko]] ('''2014'''). ''[https://www.jair.org/papers/paper4217.html Large-Scale Optimization for Evaluation Functions with Minimax Search]''. [https://www.jair.org/vol/vol49.html JAIR Vol. 49], [https://pdfs.semanticscholar.org/eb9c/173576577acbb8800bf96aba452d77f1dc19.pdf pdf] » [[Shogi]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=55084 MMTO for evaluation learning] by [[Jon Dart]], [[CCC]], January 25, 2015</ref>
* [https://scholar.google.com/citations?user=glcep6EAAAAJ&hl=en Aryan Mokhtari], [https://scholar.google.com/citations?user=7mrPM4kAAAAJ&hl=en Alejandro Ribeiro] ('''2014'''). ''RES: Regularized Stochastic BFGS Algorithm''. [https://arxiv.org/abs/1401.7625 arXiv:1401.7625] <ref> [https://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm Broyden–Fletcher–Goldfarb–Shanno algorithm from Wikipedia]</ref>
* <span id="ROCK"></span>[http://www.asl.ethz.ch/the-lab/people/person-detail.html?persid=184943 Jemin Hwangbo], [https://www.linkedin.com/in/christian-gehring-1b958395/ Christian Gehring], [http://www.asl.ethz.ch/the-lab/people/person-detail.html?persid=186652 Hannes Sommer], [http://www.asl.ethz.ch/the-lab/people/person-detail.html?persid=29981 Roland Siegwart], [http://www.adrl.ethz.ch/doku.php/adrl:people:jbuchli Jonas Buchli] ('''2014'''). ''ROCK∗ — Efficient black-box optimization for policy learning''. [http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7028729 Humanoids, 2014] » [[Automated Tuning#Rockstar|Rockstar]]
* [[Mathematician#YDauphin|Yann Dauphin]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Mathematician#CGulcehre|Caglar Gulcehre]], [[Mathematician#KCho|Kyunghyun Cho]], [[Mathematician#SGanguli|Surya Ganguli]], [[Mathematician#YBengio|Yoshua Bengio]] ('''2014'''). ''Identifying and attacking the saddle point problem in high-dimensional non-convex optimization''. [https://arxiv.org/abs/1406.2572 arXiv:1406.2572] <ref>[https://groups.google.com/d/msg/fishcooking/wOfRuzTSi_8/VgjN8MmSBQAJ high dimensional optimization] by [[Warren D. Smith]], [[Computer Chess Forums|FishCooking]], December 27, 2019</ref>
* [https://arxiv.org/find/cs/1/au:+Martens_J/0/1/0/all/0/1 James Martens] ('''2014, 2017'''). ''New insights and perspectives on the natural gradient method''. [https://arxiv.org/abs/1412.1193 arXiv:1412.1193]
==2015 ...==
'''2016'''
* [[Diogo Real]], [[Alan Blair]] ('''2016'''). ''[https://ieeexplore.ieee.org/document/7743850/ Learning a multi-player chess game with TreeStrap]''. [https://dblp.uni-trier.de/db/conf/cec/cec2016.html CEC 2016]
* [[Wojciech Jaśkowski]], [[Marcin Szubert]] ('''2016'''). ''[https://ieeexplore.ieee.org/document/7180338 Coevolutionary CMA-ES for Knowledge-Free Learning of Game Position Evaluation]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. 8, No. 4 <ref>[https://en.wikipedia.org/wiki/CMA-ES CMA-ES from Wikipedia]</ref>
* [[Wojciech Jaśkowski]], [[Paweł Liskowski]], [[Marcin Szubert]], [[Krzysztof Krawiec]] ('''2016'''). ''[https://content.sciendo.com/view/journals/amcs/26/1/article-p215.xml The performance profile: A multi–criteria performance evaluation method for test–based problems]''. [https://en.wikipedia.org/wiki/International_Journal_of_Applied_Mathematics_and_Computer_Science International Journal of Applied Mathematics and Computer Science], Vol. 26, No. 1
'''2017'''
* [http://ruder.io/ Sebastian Ruder] ('''2017'''). ''[http://ruder.io/optimizing-gradient-descent/ An overview of gradient descent optimization algorithms]''. [https://arxiv.org/abs/1609.04747v2 arXiv:1609.04747v2] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=64189&start=46 Re: Texel tuning method question] by [[Jon Dart]], [[CCC]], July 23, 2017</ref>
* [[Hung-Jui Chang]], [[Jr-Chang Chen]], [[Gang-Yu Fan]], [[Chih-Wen Hsueh]], [[Tsan-sheng Hsu]] ('''2018'''). ''Using Chinese dark chess endgame databases to validate and fine-tune game evaluation functions''. [[ICGA Journal#40_2|ICGA Journal, Vol. 40, No. 2]] » [[Chinese Dark Chess]], [[Endgame Tablebases]]
* [[Wen-Jie Tseng]], [[Jr-Chang Chen]], [[I-Chen Wu]], [[Tinghan Wei]] ('''2018'''). ''Comparison Training for Computer Chinese Chess''. [https://arxiv.org/abs/1801.07411 arXiv:1801.07411] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=52861&start=7 Re: multi-dimensional piece/square tables] by Tony P., [[CCC]], January 28, 2020 » [[Piece-Square Tables]]</ref>
* [[Jeremy Rapin]], [[Olivier Teytaud]] ('''2018'''). ''Nevergrad - A gradient-free optimization platform''. [https://github.com/facebookresearch/nevergrad GitHub - facebookresearch/nevergrad: A Python toolbox for performing gradient-free optimization]
==2020 ...==
* [[Andrew Grant]] ('''2020'''). ''Evaluation & Tuning in Chess Engines''. [https://github.com/AndyGrant/Ethereal/blob/master/Tuning.pdf pdf] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74877 Evaluation & Tuning in Chess Engines] by [[Andrew Grant]], [[CCC]], August 24, 2020</ref>
=Forum Posts=
* [https://www.stmintz.com/ccc/index.php?id=487022 "learning" or "tuning" programs] by [[Sean Mintz]], [[CCC]], February 15, 2006
* [http://www.open-aurec.com/wbforum/viewtopic.php?f=4&t=49450 Adjusting weights the Deep Blue way] by [[Tony van Roon-Werten]], [[Computer Chess Forums|Winboard Forum]], August 29, 2008 » [[Deep Blue]]
: [http://www.open-aurec.com/wbforum/viewtopic.php?f=4&t=49450&start=3 Re: Adjusting weights the Deep Blue way] by [[Pradu Kannan]], [[Computer Chess Forums|Winboard Forum]], September 01, 2008
* [http://www.open-aurec.com/wbforum/viewtopic.php?f=4&t=49818 Tuning the eval] by [[Daniel Anulliero]], [[Computer Chess Forums|Winboard Forum]], January 02, 2009
* [http://www.talkchess.com/forum/viewtopic.php?t=27266 Insanity... or Tal style?] by [[Miguel A. Ballicora]], [[CCC]], April 01, 2009
* [http://www.talkchess.com/forum/viewtopic.php?t=62012 CLOP: when to stop?] by [[Erin Dame]], [[CCC]], November 07, 2016 » [[CLOP]]
: [http://www.talkchess.com/forum/viewtopic.php?t=62012&start=6 Re: CLOP: when to stop?] by [[Álvaro Begué]], [[CCC]], November 08, 2016 <ref>[https://en.wikipedia.org/wiki/Limited-memory_BFGS Limited-memory BFGS from Wikipedia]</ref>
* [http://www.talkchess.com/forum/viewtopic.php?t=62056 C++ code for tuning evaluation function parameters] by [[Álvaro Begué]], [[CCC]], November 10, 2016 » [[RuyTune]] <ref>[https://bitbucket.org/alonamaloh/ruy_tune alonamaloh / ruy_tune — Bitbucket] by [[Álvaro Begué]]</ref>
'''2017'''
* [http://www.talkchess.com/forum/viewtopic.php?t=63408 improved evaluation function] by [[Alexandru Mosoi]], [[CCC]], March 11, 2017 » [[Texel's Tuning Method]], [[Zurichess]]
* [http://www.talkchess.com/forum/viewtopic.php?t=66221 tuning info] by [[Marco Belli]], [[CCC]], January 03, 2018
* [http://www.talkchess.com/forum/viewtopic.php?t=66681 3 million games for training neural networks] by [[Álvaro Begué]], [[CCC]], February 24, 2018 » [[Neural Networks]]
* [https://groups.google.com/d/msg/fishcooking/XnLmUP_78iw/QgMZzmeVBgAJ Tuning floats] by [[Stephane Nicolet]], [[Computer Chess Forums|FishCooking]], April 12, 2018
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=67831 Introducing PET] by [[Ed Schroder|Ed Schröder]], [[CCC]], June 27, 2018 » [[Strategic Test Suite]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=68326 Texel tuning speed] by [[Vivien Clauzon]], [[CCC]], August 29, 2018 » [[Texel's Tuning Method]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=68753 methods for tuning coefficients] by [[Stuart Cracraft]], [[CCC]], October 28, 2018
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69035 Particle Swarm Optimization Code] by [[Erik Madsen]], [[CCC]], November 24, 2018 » [[MadChess]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=69207 Gradient Descent Introduction] by [[Michael Hoffmann|Desperado]], [[CCC]], December 09, 2018
'''2019'''
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=69532 Automated tuning... finally... (Topple v0.3.0)] by [[Vincent Tang]], [[CCC]], January 08, 2019 » [[Topple]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=71650 New Tool for Tuning with Skopt] by [[Thomas Dybdahl Ahle]], [[CCC]], August 25, 2019 <ref>[https://scikit-optimize.github.io/ skopt API documentation]</ref>
* [https://www.game-ai-forum.org/viewtopic.php?f=21&t=695 TD(1)] by [[Rémi Coulom]], [[Computer Chess Forums|Game-AI Forum]], November 20, 2019 » [[Temporal Difference Learning]]
* [https://groups.google.com/d/msg/fishcooking/wOfRuzTSi_8/VgjN8MmSBQAJ high dimensional optimization] by [[Warren D. Smith]], [[Computer Chess Forums|FishCooking]], December 27, 2019 <ref>[[Mathematician#YDauphin|Yann Dauphin]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Mathematician#CGulcehre|Caglar Gulcehre]], [[Mathematician#KCho|Kyunghyun Cho]], [[Mathematician#SGanguli|Surya Ganguli]], [[Mathematician#YBengio|Yoshua Bengio]] ('''2014'''). ''Identifying and attacking the saddle point problem in high-dimensional non-convex optimization''. [https://arxiv.org/abs/1406.2572 arXiv:1406.2572]</ref>
==2020 ...==
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=72810 Board adaptive / tuning evaluation function - no NN/AI] by Moritz Gedig, [[CCC]], January 14, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=73629 Pawn structure tuning] by [[Vivien Clauzon]], [[CCC]], April 11, 2020 » [[Pawn Structure]], [[Ethereal]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74184 Learning/Tuning in SlowChess Blitz Classic] by [[Jonathan Kreuzer]], [[CCC]], June 15, 2020 » [[Slow Chess]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74209 Great input about Bayesian optimization of noisy function methods] by [[Vivien Clauzon]], [[CCC]], June 16, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74877 Evaluation & Tuning in Chess Engines] by [[Andrew Grant]], [[CCC]], August 24, 2020 » [[Ethereal]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74955 Train a neural network evaluation] by [[Fabio Gobbato]], [[CCC]], September 01, 2020 » [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75012 Speeding Up The Tuner] by [[Dennis Sceviour]], [[CCC]], September 06, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75104 Yet another parameter tuner using optuna framework] by [[Ferdinand Mosca]], [[CCC]], September 14, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75104&start=15 Re: Yet another parameter tuner using optuna framework] by [[Karlson Pfannschmidt]], [[CCC]], September 16, 2020 <ref>[https://optunity.readthedocs.io/en/latest/user/solvers/TPE.html Tree-structured Parzen Estimator — Optunity 1.1.0 documentation]</ref>
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75234 evaluation tuning - where to start?] by [[Maksim Korzh]], [[CCC]], September 27, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75267 How to calculate piece weights with logistic regression?] by [[Maksim Korzh]], [[CCC]], October 01, 2020 » [[Automated Tuning#Regression|Regression]], [[Point Value by Regression Analysis]], [[Point Value]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75411 Unsupervised reinforcement tuning from zero] by Madeleine Birchfield, [[CCC]], October 16, 2020 » [[Reinforcement Learning]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76024 Laskas parameter optimizer] by [[Ferdinand Mosca]], [[CCC]], December 09, 2020
'''2021'''
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76227 How to calc the derivative for gradient descent?] by Brian Neal, [[CCC]], January 04, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76238 Help with Texel's tuning] by [[Maksim Korzh]], [[CCC]], January 05, 2021 » [[Texel's Tuning Method]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76265 Tapered Evaluation and MSE (Texel Tuning)] by [[Michael Hoffmann]], [[CCC]], January 10, 2021 » [[Texel's Tuning Method]], [[Tapered Eval]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76288 Training data] by [[Michael Hoffmann]], [[CCC]], January 12, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76292 Why using the game result instead of evaluation scores] by [[Michael Hoffmann]], [[CCC]], January 12, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76294 Using Mini-Batch for tunig] by [[Michael Hoffmann]], [[CCC]], January 12, 2021
=External Links=
: [https://en.wikipedia.org/wiki/Engine_tuning Engine tuning from Wikipedia]
: [https://en.wikipedia.org/wiki/Self-tuning Self-tuning from Wikipedia]
==Engine Tuning==
* [https://www.3dkingdoms.com/chess/learning.html Automatic Tuning & Learning for Slow Chess Blitz Classic] by by [[Jonathan Kreuzer]] » [[Slow Chess]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74184 Learning/Tuning in SlowChess Blitz Classic] by [[Jonathan Kreuzer]], [[CCC]], June 15, 2020</ref>
* [https://chess-tuning-tools.readthedocs.io/en/latest/ Chess Tuning Tools] by [[Karlson Pfannschmidt]]
* [http://rebel13.nl/rebel13/pet.html Practical Engine Tuning] by [[Ed Schroder|Ed Schröder]], June 2018 » [[Strategic Test Suite]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=67831 Introducing PET] by [[Ed Schroder|Ed Schröder]], [[CCC]], June 27, 2018</ref>
==Optimization==
: [https://en.wiktionary.org/wiki/optimize optimize - Wiktionary]
* [https://en.wikipedia.org/wiki/Mathematical_optimization Mathematical optimization from Wikipedia]
* [https://en.wikipedia.org/wiki/Operations_research Operations research from Wikipedia]
* [https://en.wikipedia.org/wiki/Optimization_problem Optimization problem from Wikipedia]
* [https://en.wikipedia.org/wiki/Duality_(optimization) Duality (optimization) from Wikipedia]
* [https://en.wikipedia.org/wiki/Local_search_%28optimization%29 Local search (optimization) from Wikipedia]
* [https://en.wikipedia.org/wiki/Iterated_local_search Iterated local search from Wikipedia]
: [https://en.wikipedia.org/wiki/Entropy_maximization Entropy maximization from Wikipedia]
: [https://en.wikipedia.org/wiki/Linear_programming Linear programming from Wikipedia]
: [https://en.wikipedia.org/wiki/Nonlinear_programming Nonlinear programming from Wikipedia]
: [https://en.wikipedia.org/wiki/Simplex_algorithm Simplex algorithm from Wikipedia]
* [https://en.wikipedia.org/wiki/Differential_evolution Differential evolution from Wikipedia]
: [https://en.wikipedia.org/wiki/Stochastic_approximation Stochastic approximation from Wikipedia]
: [https://en.wikipedia.org/wiki/Stochastic_gradient_descent Stochastic gradient descent from Wikipedia]
: [https://en.wikipedia.org/wiki/Stochastic_gradient_descent#AdaGrad AdaGrad from Wikipedia]
==Machine Learning==
* [https://en.wikipedia.org/wiki/Machine_learning Machine learning from Wikipedia]
* [https://en.wikipedia.org/wiki/Tikhonov_regularization Tikhonov regularization (Ridge regression) from Wikipedia]
==Code==
* [https://bitbucketgithub.orgcom/alonamalohfacebookresearch/ruy_tune alonamaloh nevergrad GitHub - facebookresearch/ ruy_tune — Bitbucket] » [[RuyTune]] by [[Álvaro Begué]nevergrad: A Python toolbox for performing gradient-free optimization]* <span id="Rockstar"></span>[https://github.com/lantonovfsmosca/Rockstar RockstarOptuna-Game-Parameter-Tuner GitHub - fsmosca/Optuna-Game-Parameter-Tuner: Implementation of ROCK* algorithm (Gaussian kernel regression + natural gradient descent) for optimisation | GitHubA game search and evaluation parameter tuner using optuna framework] by [[Lyudmil Antonov]] and [[Joona KiiskiFerdinand Mosca]] » [[Automated Tuning#ROCK|ROCK*Deuterium]] <ref>[http://www.talkchess.com/forumforum3/viewtopic.php?f=2&t=65045 ROCK* black-box optimizer for chess75104 Yet another parameter tuner using optuna framework] by [[Jon DartFerdinand Mosca]], [[CCC]], August 31September 14, 20172020</ref>* [https://github.com/zamarfsmosca/spsa SPSA Tuner for Stockfish Chess Engine | Lakas GitHub- fsmosca/Lakas: Game parameter optimizer using nevergrad framework] by [[Joona KiiskiFerdinand Mosca]]<ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76024 Laskas parameter optimizer] » by [[StockfishFerdinand Mosca]], [[Stockfish's Tuning MethodCCC]]* [https:, December 09, 2020<//github.com/scikit-optimize/scikit-optimize GitHub - scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface]ref>
* [https://github.com/kiudee/bayes-skopt GitHub - kiudee/bayes-skopt: A fully Bayesian implementation of sequential model-based optimization] by [[Karlson Pfannschmidt]] » [[Fat Fritz]] <ref>[https://en.chessbase.com/post/fat-fritz-update-and-fat-fritz-jr Fat Fritz 1.1 update and a small gift] by [[Albert Silver]]. [[ChessBase|ChessBase News]], March 05, 2020</ref>
* [https://github.com/kiudee/chess-tuning-tools GitHub - kiudee/chess-tuning-tools] by [[Karlson Pfannschmidt]] » [[Leela Chess Zero]]
* [https://github.com/krasserm/bayesian-machine-learning GitHub - krasserm/bayesian-machine-learning: Notebooks about Bayesian methods for machine learning] by [https://krasserm.github.io/ Martin Krasser] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74209 Great input about Bayesian optimization of noisy function methods] by [[Vivien Clauzon]], [[CCC]], June 16, 2020</ref>
* [https://github.com/thomasahle/noisy-bayesian-optimization GitHub - thomasahle/noisy-bayesian-optimization: Bayesian Optimization for very Noisy functions] by [[Thomas Dybdahl Ahle]] » [[FastChess]]
* [https://github.com/scikit-optimize/scikit-optimize GitHub - scikit-optimize/scikit-optimize: Sequential model-based optimization with a `scipy.optimize` interface]
* <span id="Rockstar"></span>[https://github.com/lantonov/Rockstar Rockstar: Implementation of ROCK* algorithm (Gaussian kernel regression + natural gradient descent) for optimisation | GitHub] by [[Lyudmil Antonov]] and [[Joona Kiiski]] » [[Automated Tuning#ROCK|ROCK*]] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=65045 ROCK* black-box optimizer for chess] by [[Jon Dart]], [[CCC]], August 31, 2017</ref>
* [https://github.com/zamar/spsa SPSA Tuner for Stockfish Chess Engine | GitHub] by [[Joona Kiiski]] » [[Stockfish]], [[Stockfish's Tuning Method]]
==Misc==
* [[:Category:The Next Step Quintet|The Next Step Quintet]] feat. [http://www.tivonpennicott.com/ Tivon Pennicott] - [http://www.discogs.com/Next-Step-Quintet-The-Next-Step-Quintet/release/4970720 Regression], [https://el-gr.facebook.com/KerameioBar KerameioBar] [https://en.wikipedia.org/wiki/Athens Athens], [https://en.wikipedia.org/wiki/Greece Greece], September 2014, [https://en.wikipedia.org/wiki/YouTube YouTube] Video

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