Monte Carlo tree search. When the NPM search space can be framed as a tree, Monte Carlo tree search [ ] is more efficient than random search. Starting at the root node, a policy to select a child node is iteratively applied, giving a path through the tree, culminating at a leaf node. The experiment is then conducted on the NPM represented by the leaf node. Its measured property, viewed as a reward, is back-propagated through the tree to update the statistics of each node along the path to it. Both the visit counts and reward allocations of the nodes are used in the tree policy to balance exploration of new branches of the tree that have not been visited often (or at all) and exploitation of current knowledge to follow branches of the tree that appear likely to lead to optimal NPMs. See Refs. as examples.