Topic
Incremental heuristic search
About: Incremental heuristic search is a research topic. Over the lifetime, 2376 publications have been published within this topic receiving 89502 citations.
Papers published on a yearly basis
Papers
More filters
500 citations
TL;DR: This study compares the hybrid algorithms in terms of solution quality and computation time on a number of packing problems of different size and shows the effectiveness of the design of the different algorithms.
487 citations
TL;DR: Two progressive strategies for MCTS are introduced, called progressive bias and progressive unpruning, which enable the use of relatively time-expensive heuristic knowledge without speed reduction.
Abstract: Monte-Carlo Tree Search (MCTS) is a new best-first search guided by the results of Monte-Carlo simulations. In this article, we introduce two progressive strategies for MCTS, called progressive bias and progressive unpruning. They enable the use of relatively time-expensive heuristic knowledge without speed reduction. Progressive bias directs the search according to heuristic knowledge. Progressive unpruning first reduces the branching factor, and then increases it gradually again. Experiments assess that the two progressive strategies significantly improve the level of our Go program Mango. Moreover, we see that the combination of both strategies performs even better on larger board sizes.
458 citations
TL;DR: In this article, a simple local search heuristic was proposed to obtain polynomial-time approximation bounds for metric versions of the k-median problem and the uncapacitated facility location problem.
441 citations
Proceedings Article•
27 Jul 1997TL;DR: This work presents two statistical measures of the local search process that allow one to quickly find the optimal noise settings, and applies these principles to the problem of evaluating new search heuristics, and discovered two promising new strategies.
Abstract: It is well known that the performance of a stochastic local search procedure depends upon the setting of its noise parameter, and that the optimal setting varies with the problem distribution. It is therefore desirable to develop general priniciples for tuning the procedures. We present two statistical measures of the local search process that allow one to quickly find the optimal noise settings. These properties are independent of the fine details of the local search strategies, and appear to be relatively independent of the structure of the problem domains. We applied these principles to the problem of evaluating new search heuristics, and discovered two promising new strategies.
431 citations