Blondie25

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Blondie25,

an evolutionary chess program by David B. Fogel and Timothy J. Hays, supported by James Quon and Sarah L. Hahn. Blondie25 improved its play by almost 400 rating points during evolution [1].

New Results in Evolving Chess

Quote from the Press Release, May 22, 2006 [2]

Blondie25 is the result of over 8000 generations of variation and selection, simulated on a computer, in which a computer chess-playing program plays games against variations of itself to learn how to improve its play. Blondie25 includes mechanisms for [learning](Learning "Learning") the [values of the pieces](Point_Value "Point Value"), their [locations](Piece-Square_Tables "Piece-Square Tables") on the chessboard, and also uses [neural networks](Neural_Networks "Neural Networks") to assess the formation of pieces in different areas of the board. 

See also

Publications

References

  1. David B. Fogel, Timothy J. Hays, Sarah L. Hahn, James Quon (2004). A Self-Learning Evolutionary Chess Program. Proceedings of the IEEE, Vol. 92 No. 12, CiteSeerX
  2. Natural Selection, Inc. Presents New Results in Evolving Chess Programs: Two Milestones in Self-Learning Chess Achieved | Natural Selection, Inc (as of October 24, 2018 no longer avalable)

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