breadth first search - meaning and definition. What is breadth first search
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What (who) is breadth first search - definition

ALGORITHM FOR SEARCHING THE NODES OF A GRAPH IN ORDER BY THEIR HOP COUNT FROM A STARTING NODE
Breadth first search; Breadth first recursion; Breadth-first traversal; BFS algorithm; Breadth-first; Breath first search; Breath-first search; Breadth-First Search; Applications of breadth-first search
  • BFS on [[Maze-solving algorithm]]
  • Top part of [[Tic-tac-toe]] game tree

breadth-first search         
<algorithm> A graph search algorithm which tries all one-step extensions of current paths before trying larger extensions. This requires all current paths to be kept in memory simultaneously, or at least their end points. Opposite of depth-first search. See also {best first search}. (1996-01-05)
Parallel breadth-first search         
  • 2D-partition of the adjacency matrix.
  •  An example of bag structure with 23 elements.
  •  An example of CSR representation of a directed graph.
  • A distributed memory model.
  • A PRAM Model.
  •  Pennant data structure for k=0 to k=3.
  • A shared memory model.
User:Kit unodc/sandbox; Draft:Serial Breadth-First-Search; Draft:Parallel Breadth-First-Search; Parallel Breadth-First-Search
The breadth-first-search algorithm is a way to explore the vertexes of a graph layer by layer. It is a basic algorithm in graph theory which can be used as a part of other graph algorithms.
Lexicographic breadth-first search         
LexBFS; Lexicographic BFS
In computer science, lexicographic breadth-first search or Lex-BFS is a linear time algorithm for ordering the vertices of a graph. The algorithm is different from a breadth-first search, but it produces an ordering that is consistent with breadth-first search.

Wikipedia

Breadth-first search

Breadth-first search (BFS) is an algorithm for searching a tree data structure for a node that satisfies a given property. It starts at the tree root and explores all nodes at the present depth prior to moving on to the nodes at the next depth level. Extra memory, usually a queue, is needed to keep track of the child nodes that were encountered but not yet explored.

For example, in a chess endgame a chess engine may build the game tree from the current position by applying all possible moves, and use breadth-first search to find a win position for white. Implicit trees (such as game trees or other problem-solving trees) may be of infinite size; breadth-first search is guaranteed to find a solution node if one exists.

In contrast, (plain) depth-first search, which explores the node branch as far as possible before backtracking and expanding other nodes, may get lost in an infinite branch and never make it to the solution node. Iterative deepening depth-first search avoids the latter drawback at the price of exploring the tree's top parts over and over again. On the other hand, both depth-first algorithms get along without extra memory.

Breadth-first search can be generalized to graphs, when the start node (sometimes referred to as a 'search key') is explicitly given, and precautions are taken against following a vertex twice.

BFS and its application in finding connected components of graphs were invented in 1945 by Konrad Zuse, in his (rejected) Ph.D. thesis on the Plankalkül programming language, but this was not published until 1972. It was reinvented in 1959 by Edward F. Moore, who used it to find the shortest path out of a maze, and later developed by C. Y. Lee into a wire routing algorithm (published 1961).