One application of DFS is in pathfinding and graph traversal problems. It is commonly used to find paths between nodes in a graph or to explore all nodes in a graph.

The selection sort algorithm is a simple sorting algorithm that divides the input list into two parts: a sorted and an unsorted portion. It repeatedly selects the smallest (or largest) element from the unsorted part and swaps it with the first element of the unsorted part.

An efficient way to handle deletions in a hash table is to use a special marker value to mark deleted entries. This allows for proper rehashing and ensures that the deleted entries are correctly accounted for during subsequent operations.

Memoization optimizes the computation of Fibonacci numbers by storing previously calculated values in a table (memory). When a Fibonacci number is needed, the algorithm first checks if it's already in the table, and if so, retrieves the precomputed value, avoiding redundant recursive calculations.

Both Insertion Sort and Bubble Sort are comparison-based sorting algorithms, but their approaches differ. Insertion Sort builds the sorted part of the array one element at a time, while Bubble Sort repeatedly steps through the list.

In this scenario, a Red-Black Tree would be chosen. Red-Black Trees provide a good balance between the search and insertion/deletion operations, ensuring that the tree remains balanced. Their self-balancing property makes them suitable for scenarios with frequent modifications while maintaining a relatively balanced structure.

The time complexity of the dynamic programming solution for the coin change problem is O(n * m), where 'n' is the target amount and 'm' is the number of coin denominations. This is because the dynamic programming table has dimensions n x m, and each entry is filled in constant time.

The Longest Increasing Subsequence (LIS) problem can be efficiently solved using Binary Search. The binary search approach allows us to find the length of the LIS in an optimized way, reducing the time complexity.

To handle multiple strings in the longest common substring problem, dynamic programming can be extended to a multidimensional array. This array helps store the common substrings for each pair of strings, and the results can then be combined.

BFS guarantees finding the shortest path in an unweighted graph because it explores nodes in increasing order. As it systematically traverses nodes level by level, the first time a node is encountered, it is reached through the shortest path.

Binary search is more efficient for large datasets because it divides the search space in half at each step, resulting in a time complexity of O(log n), which is significantly faster than linear search (O(n)).

Merge sort is a Divide and Conquer sorting algorithm that follows the Divide and Conquer strategy. It recursively divides the array into two halves, sorts them, and then merges them back together.