Generalized Nested Rollout Policy Adaptation with Limited Repetitions
This incremental improvement addresses optimization inefficiencies in sequence-based combinatorial problems for researchers and practitioners in computational biology and operations research.
The authors tackled the issue of deterministic policies in Generalized Nested Rollout Policy Adaptation (GNRPA) by limiting repetitions of the best sequence, resulting in improved performance for combinatorial problems like Inverse RNA Folding, TSP with Time Windows, and the Weak Schur problem.
Generalized Nested Rollout Policy Adaptation (GNRPA) is a Monte Carlo search algorithm for optimizing a sequence of choices. We propose to improve on GNRPA by avoiding too deterministic policies that find again and again the same sequence of choices. We do so by limiting the number of repetitions of the best sequence found at a given level. Experiments show that it improves the algorithm for three different combinatorial problems: Inverse RNA Folding, the Traveling Salesman Problem with Time Windows and the Weak Schur problem.