Approaches

Evolutionary optimisation

Tune a player's weights by playing complete games, keeping the settings that scored best, and repeating.

An evolved NNUE leaf, distilled from a depth-5 teacher

A compact neural leaf evaluator for the depth-3 fair search, warm-started by distilling a depth-5 teacher's sibling-complete root values and then refined by an evolutionary search whose fitness is whole-game score itself.

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Evolutionary optimisationfeatured

Evolution

Let a search tune board-feature weights by playing thousands of games, keeping the best weights, and repeating.

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Evolutionary optimisation

Direct public policy

Skip the value model. Score each column with 25 weighted features and tune the weights against whole games.

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Evolutionary optimisation

Evolved public policy

Give the weight search a larger phase-aware evaluator and optimise complete games directly, without imitation or proxy labels.

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Evolutionary optimisation

Evolving the leaf weights at the depth they are used

Evolves the reference evaluator on complete games, then tests the frozen winner once on unseen games.

Evolutionary optimisation

Machine-tuning the board evaluator

Let an optimiser adjust eight coefficients of the search's board evaluator by playing complete games, then freeze the winner and test it on games it never saw.

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