What type of search does the Backtracking algorithm implement?

The Backtracking algorithm implements a type of search that is fundamentally aligned with Depth First Search (DFS) principles. In backtracking, the algorithm explores each branch of a solution tree to its fullest before returning to explore alternative branches. This is characteristic of DFS, where the algorithm goes deep into one path until it hits a dead end or finds a solution, at which point it backtracks to the last decision point to try a different option.

Backtracking is particularly useful in solving constraint satisfaction problems, such as puzzles and combinatorial tasks. It systematically searches for a solution by attempting to build a solution incrementally, removing those parts that fail to satisfy the constraints of the problem—much like how DFS navigates through a graph or tree structure by deeply exploring one path at a time.

In contrast, Sequential search, Breadth First Search (BFS), and Binary Search all involve different strategies or mechanisms for searching. Sequential search examines each element one-by-one, whereas BFS explores all of the neighbor nodes at the present depth before moving on to nodes at the next depth level, following a wider approach. Binary Search, on the other hand, operates on sorted data by repeatedly dividing the search interval in half, which is a fundamentally different concept from the exhaustive search approach