# Beam-Search Algorithm

A Beam-Search Algorithm is a best-first search algorithm that retains only a predetermined number of candidate partial solutions.

**Context:**- It can be implemented by a Beam Search-based System.

**Example(s):****Counter-Example(s):****See:**Learning as Search, Viterbi Algorithm, Breadth-First Search.

## References

### 2012

- http://en.wikipedia.org/wiki/Beam_search
- In computer science,
**beam search**is a heuristic search algorithm that explores a graph by expanding the most promising node in a limited set. Beam search is an optimization of best-first search that reduces its memory requirements. Best-first search is a graph search which orders all partial solutions (states) according to some heuristic which attempts to predict how close a partial solution is to a complete solution (goal state). But in beam search, only a predetermined number of best partial solutions are kept as candidates.Beam search uses breadth-first search to build its search tree. At each level of the tree, it generates all successors of the states at the current level, sorting them in increasing order of heuristic cost. However, it only stores a predetermined number of best states at each level (called the beam width). Only those states are expanded next. The greater the beam width, the fewer states are pruned. With an infinite beam width, no states are pruned and beam search is identical to breadth-first search. The beam width bounds the memory required to perform the search. Since a goal state could potentially be pruned, beam search sacrifices completeness (the guarantee that an algorithm will terminate with a solution, if one exists) and optimality (the guarantee that it will find the best solution).

The beam width can either be fixed or variable. One approach that uses a variable beam width starts with the width at a minimum. If no solution is found, the beam is widened and the procedure is repeated.

- In computer science,

### 2011

- (Sammut, 2011) ⇒ Claude Sammut. (2011). “Beam Search.” In: (Sammut & Webb, 2011) p.93