Topic
Depth-first search
About: Depth-first search is a research topic. Over the lifetime, 635 publications have been published within this topic receiving 17696 citations. The topic is also known as: DFS & depth-first traversal.
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TL;DR: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples of an improved version of an algorithm for finding the strongly connected components of a directed graph.
Abstract: The value of depth-first search or “backtracking” as a technique for solving problems is illustrated by two examples. An improved version of an algorithm for finding the strongly connected componen...
5,660 citations
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.
1,698 citations
01 Jan 2010
TL;DR: An overview of very large scale neighborhood search methods is given and recent variants and extensions like variable depth search and adaptive large neighborhood search are discussed.
Abstract: Heuristics based on large neighborhood search have recently shown outstanding results in solving various transportation and scheduling problems. Large neighborhood search methods explore a complex neighborhood by use of heuristics. Using large neighborhoods makes it possible to find better candidate solutions in each iteration and hence traverse a more promising search path. Starting from the large neighborhood search method, we give an overview of very large scale neighborhood search methods and discuss recent variants and extensions like variable depth search and adaptive large neighborhood search.
482 citations
IBM1
TL;DR: An algorithm for mining long patterns in databases by using depth first search on a lexicographic tree of itemsets achieves more than one order of magnitude speedup over the recently proposed MaxMiner algorithm.
Abstract: In this paper we present an algorithm for mining long patterns in databases. The algorithm nds large itemsets by using depth rst search on a lexicographic tree of itemsets. The focus of this paper is to develop CPU-e cient algorithms for nding frequent itemsets in the cases when the database contains patterns which are very wide. We refer to this algorithm as DepthProject, and it achieves more than one order of magnitude speedup over the recently proposed MaxMiner algorithm for nding long patterns. These techniques may be quite useful for applications in areas such as computational biology in which the number of records is relatively small, but the itemsets are very long. This necessitates the discovery of patterns using algorithms which are especially tailored to the nature of such domains.
362 citations
TL;DR: A hybrid procedure that embeds GLS (Guided Local Search) into a Shifting Bottleneck framework and takes advantage of the differences between the two neighborhood structures proves to be particularly efficient.
Abstract: Many recently developed local search procedures for job shop scheduling use interchange of operations, embedded in a simulated annealing or tabu search framework. We develop a new variable depth search procedure, GLS (Guided Local Search), based on an interchange scheme and using the new concept of neighborhood trees. Structural properties of the neighborhood are used to guide the search in promising directions. While this procedure competes successfully with others even as a stand-alone, a hybrid procedure that embeds GLS into a Shifting Bottleneck framework and takes advantage of the differences between the two neighborhood structures proves to be particularly efficient. We report extensive computational testing on all the problems available from the literature.
355 citations