Understanding Beam Search Algorithm Beam search is a variation of the best-first search algorithm, designed to optimize space complexity. Unlike best-first search which maintains all explored nodes in memory and sorts them globally, beam search limits this by keeping only a fixed number (beam width) of the most promising nodes based on heuristic values. This pruning reduces memory usage significantly while still exploring potential paths efficiently.
Mechanics and Heuristic Evaluation In beam search, starting from an initial state with multiple options, only the top 'n' choices are retained according to their heuristic scores—lower costs indicating better paths. For example, if three options have respective costs 32 (B), 25 (C), and 35 (D), retaining two means selecting C and B for further exploration while discarding D. The process repeats at each level using sorting methods like merge or quick sort for efficiency.
Advantages Over Best-First Search The primary advantage of beam search lies in its constant space complexity due to node pruning based on predefined beta/beam value rather than storing all possibilities as done in best-first searches where branching factor leads to exponential growth in time/space requirements. Sorting fewer elements also improves computational speed compared with handling larger global lists.
'Completeness' Limitation & Special Cases While efficient regarding resources used during execution, beam searches lack completeness; they may discard optimal solutions prematurely when pruned early stages lead towards dead ends instead viable outcomes later missed entirely under strict constraints imposed narrow beams widths chosen heuristically beforehand experimentation backend processes determining suitable parameters specific scenarios applications discussed future videos hill climbing method related variant single-node retention strategy explained upcoming content thank watching