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a [[Supervised Learning|supervised]] [[Automated Tuning|tuning method]] based on [[Automated Tuning#MoveAdaption|move adaptation]],
dubbed the '''Bonanza Method''' which evolved to [[Minimax Tree Optimization]] (MMTO).
Bonanza utilizes and tuned [[Piece-Square Tables|piece-square tables]] indexed by king location and further two-piece locations and side to move (turn), dubbed '''KPP''', '''KKP''' or '''KPPT''', which was used in many other Shogi programs <ref>[https://groups.google.com/d/msg/shogi-l/c4-dY44P8Mw/M3z-RtFR-tsJ The 25th World Computer Shogi Championships] by [[Reijer Grimbergen]] on behalf of [[Takenobu Takizawa]], [[Computer Chess Forums|SHOGI-L]], February 11, 2015</ref>, and has influenced the design of [[NNUE]] <ref>[http://yaneuraou.yaneu.com/2020/05/03/%E6%A9%9F%E6%A2%B0%E5%AD%A6%E7%BF%92%E3%82%A8%E3%83%B3%E3%82%B8%E3%83%8B%E3%82%A2%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E5%B0%86%E6%A3%8Bai%E9%96%8B%E7%99%BA%E5%85%A5%E9%96%80%E3%81%9D%E3%81%AE1/ 機械学習エンジニアのための将棋AI開発入門その1 Introduction to Shogi AI development for machine learning engineers Part 1], May 03, 2020 (Japanese)</ref>.
Beside a [[Parallel Search|parallel tree search]], Bonanza is able to apply parallelization by the so called '''Consensus''' method
