A beam search is a heuristic search technique that combines elements of breadth-first and best-first searches. Like a breadth-first search, the beam search maintains a list of nodes that represent a frontier in the search space. Whereas the breadth-first adds all neighbors to the list, the beam search orders the neighboring nodes according to some heuristic and only keeps the n best, where n is the beam size. This can significantly reduce the processing and storage requirements for the search.
In machine learning, the beam search has been used in algorithms, such as AQ11 (Dietterich & Michalski, 1977).
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Dietterich, T. G., & Michalski, R. S. (1977). Learning and generalization of characteristic descriptions: Evaluation criteria and comparative review of selected methods. In Fifth international joint conference on artificial intelligence (pp. 223–231). Cambridge, MA: William Kaufmann.
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Sammut, C. (2011). Beam Search. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_68
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