Improving

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Improving is an important modifier for many search heuristics. It is a boolean flag indicating whether the static evaluation of a position has improved from the position two plys ago. Improving can be used to modify the frequency and aggressiveness of certain pruning heuristics.

Improving as a Modifier to Search Heuristics

Heuistics	Modification
Reverse Futility Pruning	Lower pruning margin when improving
Late Move Pruning	Prune more late quiet moves when not improving
ProbCut	Lower ProbCut beta threshold when improving
Null Move Pruning	More NMP when improving. This can be done by allowing pruning even when eval is under beta (and above a certain threshold) when improving.
Late Move Reductions	Reduce more when not improving / less when improving

Code Example

The following code is taken from Alexandria. Note that in case the position is in check both 2 and 4 plies ago, improving can be set to either true or false. SPRT testing should be done to determine which of the two options is optimal for a given engine.

// Improving is a very important modifier to many heuristics. It checks if our static eval has improved since our last move.
// As we don't evaluate in check, we look for the first ply we weren't in check between 2 and 4 plies ago. If we find that
// static eval has improved, or that we were in check both 2 and 4 plies ago, we set improving to true.
if(inCheck)
    improving = false;
else if ((ss - 2)->staticEval != SCORE_NONE) {
    improving = ss->staticEval > (ss - 2)->staticEval;
}
else if ((ss - 4)->staticEval != SCORE_NONE) {
    improving = ss->staticEval > (ss - 4)->staticEval;
}
else
    improving = true;

This following code is from Integral. It demonstrates one common way to adjust LMP margin with improving.

const int lmp_threshold = (3 + depth * depth) / (2 - improving);
if (is_quiet && moves_seen >= lmp_threshold) {
    move_picker.SkipQuiets();
    continue;
}