Frontier search

Abstract

The critical resource that limits the application of best-first search is memory. We present a new class of best-first search algorithms that reduce the space complexity. The key idea is to store only the Open list of generated nodes, but not the Closed list of expanded nodes. The solution path can be recovered by a divide-and-conquer technique, either as a bidirectional or unidirectional search. For many problems, frontier search dramatically reduces the memory required by best-first search. We apply frontier search to breadth-first search of sliding-tile puzzles and the 4-peg Towers of Hanoi problem, Dijkstra's algorithm on a grid with random edge costs, and the A* algorithm on the Fifteen Puzzle, the four-peg Towers of Hanoi Problem, and optimal sequence alignment in computational biology.

Efficient algorithms for pursuing moving evaders in terrains

“…This is done because the in-memory requirements become quite large with a modest number of seekers, see Section 4.3 for further discussion on storage requirements. This idea was adapted from [15]. To mark a configuration u for removal from H it must: (i) be in solution set S, and (ii) have all its neighbors in S. Hence, for a configuration u, the number C[ u] of neighbors of u, not in S, is tracked for each configuration as the algorithm progresses.…”

A Survey and Classification of A* Based Best-First Heuristic Search Algorithms

“…One application of this algorithm, in the 4-peg Towers of Hanoi problem, searched a space with more than one trillion nodes. Frontier A* was also used to the optimal sequence alignment [18] outperforming the best existing competitors.…”

A comparative analysis of parallel disk-based Methods for enumerating implicit graphs

“…Korf [9,11] introduced frontier search as a means for reducing the scope of duplicate detection to only the current level of a breadth-first search, instead of all existing states.…”