Generating Chess Puzzles with Genetic Algorithms

A demonstration of Python’s geneticalgorithm library shows how genetic algorithms can optimize arbitrary objectives by iteratively favoring candidate solutions that receive useful gradient-like feedback, contrasting smooth distance-based scoring with all-or-nothing functions that often fail to converge. The approach represents a chessboard as 64 integer-valued squares, uses python-chess to convert candidates into boards and verify position validity, and initially finds the minimal valid board containing only two kings. It then integrates the Stockfish engine into the fitness function to search for mate-in-three positions, penalizing invalid or finished games, incorrect mating distances, excessive pieces, and positions with more than one strong move so that generated boards resemble puzzles with a unique winning move. Altering the scoring criteria can produce unusual variants, such as positions packed with knights while retaining a forced mate, illustrating the flexibility of objective-driven generation. Although these synthetic puzzles are generally less realistic than those extracted from actual games, realism could potentially be improved by adding a classifier trained on large game datasets such as Lichess data.

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