Cross-entropy search over the six feature weights against whole-game lifetime, frozen and read once on the pilot cohort

A cross-entropy search over the six Klein-Friedmann feature weights against whole-game lifetime (population 64, elite 8, 30 generations of 256 paired training games, then re-selection of nine finalists on a fresh 1,024-game block) converged by generation ten and finished in 4.5 s. Its frozen unit-normalised optimum (0.429, 0.277, 0.145, 0.033, 0.736, 0.419 for lowest-column, row detonations, column detonations, tallest-column, a-clearing-1, the-disc-clears), read once on the 256 pilot games, lives 44.82 moves and scores 138973 points: +18.57 moves over uniform-random [LB95 +17.24] and +4.29 moves [LB95 +2.81], +15005 points [LB95 +9755] over the transplanted authors' weights, winning 136 of 256 paired games. So the plateau of the temporal-difference-learned weights (about 36 moves engine-learned, 40.5 transplanted) was set by the optimizer, not by the feature set. The ceiling of the six features under this search is about 45 moves and 139,000 points; fair d4s7 on the first 32 seeds still beats the optimum by +233362 points [LB95 +153370] on 30 of 32 games, so no SCREEN against depth-4 is warranted. The optimum's direction is 0.75 cosine from the transplanted vector and 0.55 from the engine-learned one; it puts its largest weight on a clearing 1 next to gray discs and nearly none on the tallest-column feature. All three theory clauses are supported as tested at pilot tier.