Give a most-efficient Tree-Search backtracking algorithm for the minimization version of the STRING CORRECTION problem Tree Search Backtracking Algorithm for Minimization Version of String Correction

Tree Search Backtracking Algorithm to Minimize Version of String Correction 

Minimizing the string correction problem means finding the minimum amount of modification required for two strings matching. Tree-Search backtracking algorithms can solve this problem. This algorithm calculates the Levenshtein distance of the strings. Katti (2015). The Levenshtein Distance is the minimal number of single-character insertions, deletions and modifications necessary to convert one string into another. The search tree can then be built. Each node represents a single-character substitution or modification that is required to transform the first string into the next string.    Each node will be recursively explored by the algorithm, which tracks the modified string. If the modified string matches that of the target string, the algorithm returns to the parent and searches for another substitute option. This process continues until there are no more modifications (Moriah & Lavi, 2002). After the search tree has been fully explored, the minimization variant of string correction returns the minimal number of changes required for two strings to match.   Tree-Search’s backtracking algorithm can be used to solve the string correction minimization problem computationally efficiently.  References  Katti, K. (2015). An overview of automatic spelling correction methods in English. Advances in Natural and Applied Sciences (9(6), 412-418.  Moriah, Y., & Lavi, T. (2021). Levenshtein’s edit distance and relevance to software engineering. Information and Software Technology. 126. 106885.