Open AccessProceedings Article
Depth-first vs best-first search
Nageshwara Rao Vempaty,Vipin Kumar,Richard E. Korf +2 more
- pp 434-440
TLDR
A model is developed to analyze the time and space complexity of these three heuristic search algorithms in terms of the heuristic branching factor and solution density and presents a new algorithm, DFS*, which is a hybrid of iterative deepening and depth-first branch-and-bound, and shows that it outperforms the other three algorithms on some problems.Abstract:
We present a comparison of three well known heuristic search algorithms: best-first search (BFS), iterative-deepening (ID), and depth-first branch-and-bound (DFBB). We develop a model to analyze the time and space complexity of these three algorithms in terms of the heuristic branching factor and solution density. Our analysis identifies the types of problems on which each of the search algorithms performs better than the other two. These analytical results are validated through experiments on different problems. We also present a new algorithm, DFS*, which is a hybrid of iterative deepening and depth-first branch-and-bound, and show that it outperforms the other three algorithms on some problems.read more
Citations
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Journal ArticleDOI
Linear-space best-first search
TL;DR: This work presents a linear-space best-first search algorithm (RBFS) that always explores new nodes in best- first order, regardless of the cost function, and expands fewer nodes than iterative deepening with a nondecreasing cost function.
Journal ArticleDOI
Anytime heuristic search
Eric A. Hansen,Rong Zhou +1 more
TL;DR: This work describes how to convert the heuristic search algorithm A* into an anytime algorithm that finds a sequence of improved solutions and eventually converges to an optimal solution.
Journal ArticleDOI
Enhanced iterative-deepening search
TL;DR: The authors show that iterative-deepening searches can be greatly improved by exploiting previously gained node information, and their methods are faster and easier to implement than previous proposals.
Journal ArticleDOI
Bidirectional heuristic search reconsidered
Hermann Kaindl,Gerhard Kainz +1 more
TL;DR: It is shown that bidirectional heuristic search is viable and consequently it is proposed that it be reconsidered, and empirical results show that bid Directional Heuristic search can be performed very efficiently and also with limited memory.
Journal ArticleDOI
Performance of linear-space search algorithms
Weixiong Zhang,Richard E. Korf +1 more
TL;DR: The average-case performance of linear-space search algorithms, including depth-first branch-and-bound (DFBnB), iterative-deepening (ID), and recursive best-first search (RBFS) is studied, and analytic results are used to explain a surprising anomaly in the performance of these algorithms, and to predict the existence of a complexity transition in the Asymmetric Traveling Salesman Problem.
References
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A note on two problems in connexion with graphs
TL;DR: A tree is a graph with one and only one path between every two nodes, where at least one path exists between any two nodes and the length of each branch is given.
Journal ArticleDOI
A Formal Basis for the Heuristic Determination of Minimum Cost Paths
TL;DR: How heuristic information from the problem domain can be incorporated into a formal mathematical theory of graph searching is described and an optimality property of a class of search strategies is demonstrated.
Journal ArticleDOI
Branch-and-Bound Methods: A Survey
Eugene L. Lawler,D. E. Wood +1 more
TL;DR: The essential features of the branch-and-bound approach to constrained optimization are described, and several specific applications are reviewed, including integer linear programming Land-Doig and Balas methods, nonlinear programming minimization of nonconvex objective functions, and the quadratic assignment problem Gilmore and Lawler methods.
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Depth-first iterative-deepening: an optimal admissible tree search
TL;DR: This heuristic depth-first iterative-deepening algorithm is the only known algorithm that is capable of finding optimal solutions to randomly generated instances of the Fifteen Puzzle within practical resource limits.
Journal ArticleDOI
Generalized best-first search strategies and the optimality of A*
Rina Dechter,Judea Pearl +1 more
TL;DR: It is shown that several known properties of A* retain their form and it is also shown that no optimal algorithm exists, but if the performance tests are confirmed to cases in which the estimates are also consistent, then A* is indeed optimal.