Tuned razor and futility margins

lantonov · snicolet · commit 443787b0d1dc · 2019-12-10T01:10:19.000+01:00
Tuning was done with Bayesian optimisation with the following parameters: Acquisition function: Expected Improvement alpha: 0.05 xi: 1e-4 TC: 60+0.6 Number of iterations: 100 Initial points: 5 Batch size: 20 games STC http://tests.stockfishchess.org/tests/view/5dee291e3cff9a249bb9e470 LLR: 2.97 (-2.94,2.94) [-1.50,4.50] Total: 19586 W: 4382 L: 4214 D: 10990 LTC http://tests.stockfishchess.org/tests/view/5dee4e273cff9a249bb9e473 LLR: 2.95 (-2.94,2.94) [0.00,3.50] Total: 38840 W: 6315 L: 6036 D: 26489 Bench: 5033242
diff --git a/src/search.cpp b/src/search.cpp
@@ -65,9 +65,9 @@ namespace {
   constexpr uint64_t ttHitAverageResolution = 1024;
 
   // Razor and futility margins
-  constexpr int RazorMargin = 661;
+  constexpr int RazorMargin = 594;
   Value futility_margin(Depth d, bool improving) {
-    return Value(198 * (d - improving));
+    return Value(232 * (d - improving));
   }
 
   // Reductions lookup table, initialized at startup
