The dynamic programming approach to solving the Edit Distance problem involves using a matrix to store precomputed values. By breaking down the problem into subproblems and leveraging the optimal solutions to smaller subproblems, this approach avoids redundant calculations and efficiently finds the minimum edit distance.

The most efficient approach in this scenario would be to apply Manacher's Algorithm individually to each string. This ensures optimal performance for each string without unnecessary complexities.

Both Prim's and Kruskal's algorithms have a time complexity of O(n log n), where 'n' is the number of vertices in the graph. This is because they both rely on sorting the edges, and sorting dominates the overall time complexity.

The primary advantage of selection sort over bubble sort is that it has less data movement. While both have the same time complexity of O(n^2), selection sort performs fewer swaps, making it more efficient in scenarios where minimizing data movement is crucial.

In the context of the longest common substring problem, a "substring" refers to a contiguous sequence of characters within a string. It can be of any length and must appear in the same order as it does in the original string.

Edit Distance is particularly useful in text processing tasks, such as automatic summarization and speech recognition. It quantifies the similarity between two strings by measuring the minimum number of single-character edits required to change one string into the other.

In a priority queue, elements are retrieved based on their priority rather than their order of insertion. Elements with higher priority are processed before those with lower priority, allowing for flexible ordering based on specific criteria.

In the Ford-Fulkerson algorithm, the residual graph is used to represent the remaining capacity in the network. It is an auxiliary graph that helps track the available capacity for flow augmentation.

When the two strings have different lengths, the Edit Distance algorithm handles the disparity by considering the shorter string's suffix as having additional characters appended to it. This allows for a proper comparison between strings of varying lengths.

Recursion and stack data structure are closely related concepts. In recursive function calls, each function call creates a new stack frame, which contains information about the function's parameters, local variables, and return address. This stack-based mechanism allows recursive functions to maintain separate instances of local variables and control flow for each invocation, ensuring proper function execution and memory management. For example, consider the recursive implementation of factorial function, where each recursive call creates a new stack frame until the base case is reached.

A* search handles the trade-off by using a weighted sum of cost and heuristic, denoted as f = g + w * h, where 'g' is the actual cost from the start state, 'h' is the heuristic estimate, and 'w' is a weight factor. Adjusting the weight influences the balance between cost and heuristic in the decision-making process.

The key difference between a static array and a dynamic array is that a static array has a fixed size set at compile-time, whereas a dynamic array can dynamically resize itself during runtime. Static arrays are typically used in languages like C, while dynamic arrays are common in languages like Python and Java.