Evolution

Stop arguing about how much each board feature is worth and let a search find the numbers — play thousands of games with slightly different weights, keep the ones that scored best, repeat.

The intuition

Every hand evaluator in this repository contains a wall of constants: −900 for peak height risk, +300 for quiet build options, −550 for adjacent 1s. Nobody derived those numbers; somebody chose them. It is an obvious idea to let the game itself choose them instead.

The method is derivative-free optimisation on complete games. Perturb the weight vector, play whole games with each variant on the same seeds so that luck cancels, rank the variants, move the distribution toward the winners, and repeat. It needs no gradient, no labels, and no theory about why a feature matters — only a lot of games.

Three programs here try three versions of that:

1. Linear — 40 features describing what a move did and what board it left, scored as a weighted sum.
2. Nonlinear — the same 40 features fed into a small 12-unit network with 505 parameters, first taught to imitate a proper search and then evolved.
3. Phase weights — instead of free parameters, seven interpretable groups of the existing reference leaf are scaled up or down together.

How it works, step by step

1. Extract features for each legal column: what the move immediately did (score, clears, reveals, crack progress, chain depth, board clear, death, level advance) and what the resulting board looks like (occupancy and projected occupancy debt, heights and roughness, cover load and altitude, readiness and trench features, low-number clogs, outcome dispersion).
2. Score each column with the candidate weights and play the best.
3. Play whole games with every candidate on shared seeds, and rank candidates by a mixture of mean and median score — deliberately not by mean alone, so that one extraordinary chain cannot dominate a generation.
4. Update the distribution toward the best candidates and repeat for a fixed number of generations.
5. Freeze the winner and test it on games it was never fitted on.

Step 5 is where all three died.

What happened, in plain English

Each version learned its training games and gave the gain back on fresh ones.

The nonlinear version failed most informatively, because it was built as a funnel that has to pass a checkpoint before it is allowed to spend real compute. It first cloned a proper search's decisions — and the clone matched almost all of the actions it was trained on while matching fewer than half of the same teacher's actions on states it had not seen. That is a textbook distribution shift, caught before a single evolutionary generation ran. It then lost its four-game screen by half, and the funnel stopped itself.

The phase-weight version is the most interesting failure, because it was directionally right. Six generations independently agreed that the reference leaf should care more about cover debt and about trigger and rise readiness — and a completely separate privileged audit later agreed with those same directions. The frozen vector still lost its screen. Turning every weight in a group up or down together cannot express the state- and action-conditional interactions the game actually needs.

The technical record

The experiment index records all three as rejected, ledger-recorded: "each family failed its whole-game gate despite inexpensive fitting or plausible feature directions."

Nonlinear engineered-feature evolution. The 12-unit tanh network with 505 parameters cloned exact depth-3 five-stratum phase-safety actions and had to reach 85% of the paired behaviour score, at least 250,000 points, and no loss of mean moves before evolution was allowed to start. It fitted 96.86% of 255 training actions but only 43.40% of 235 disjoint teacher actions. On the first paired four-game screen the exact behaviour policy scored 307,222.75 points over 91.25 moves and the nonlinear policy 148,349.25 over 47.5; clear and reveal throughput fell from 1.956/1.063 to 1.700/0.932 per move, and the paired lower bounds were −441,770 points and −121.882 moves. The funnel stopped before evolution or confirmation. Runtime 30.4 s, peak RSS 3.81 MiB. Only 0x3d… cloning/holdout and 0x3e… screen seeds were read.

Complete-game phase-weight evolution. Six antithetic generations increased the magnitude of cover debt by 18.4% and trigger/rise readiness by 26.4%, leaving release and exposure almost unchanged — directions that agree with the later matched oracle-topology audit. The frozen vector then lost its eight-game screen: the default evaluator averaged 107,709.75 points over 75.125 moves, the evolved vector 105,534.875 over 72.5. Confirmation was not run.

Both of those runs are historical 7,000-point Sequence-scored: they used the 7,000-point level award of the 30-drop rules rather than the 17,000-point award of the mode studied here, so their absolute scores are not evidence about the million-point target. The paired comparisons, the imitation rates and the throughput figures remain valid (mode-specific scoring correction). Four and eight games are small screens.

Linear engineered-feature evolution. The ledger references this program as the source of "the audited 40 action features" reused by the nonlinear experiment, but no separate result section for the linear program was located in the experiment history. Its own outcome is therefore not retained; the index's family-level rejection is what exists. Repository-verified from the source: 40 named features with per-feature bounds, a training range of 0x3d70… and a probe range of 0x4d70… that are deliberately disjoint, defaults of 12 generations × 36 candidates × 48 training games with a 64-game probe and a 750-move cap, three chance probes during fitting and seven for the final evaluation, and a ranking objective of 0.65 × mean + 0.35 × median score.

What this taught us, and what is still open

• Fitting a policy on games is not the same as improving it. All three programs improved the thing they were fitted to. The strategy catalog's conclusion is blunt about what follows: do not treat a higher fitting score from another weight sweep as a new result without a fresh, paired gate.
• A funnel that stops early is a feature. The nonlinear run cost 30 seconds because it checked whether its clone generalised before spending compute on evolution. That pattern — cheap falsification first — is what the repository asks of every experiment.
• Plausible directions are not a result either. Two independent methods agreed that cover debt and rise readiness are undervalued, and acting on that agreement still made the policy worse. Global rescaling of a weight group is too blunt an instrument to test the hypothesis it appears to test.
• What was ruled out is these parameterisations, fitted this way. Nothing here rules out learning a leaf — but the successors that tried it, with far more capacity, hit the same wall from the same direction.