The worst-case time complexity of bubble sort is O(n^2), where 'n' is the number of elements in the array. This is due to the nested loops that iterate over the elements, making it inefficient.

The Ford-Fulkerson algorithm aims to find the maximum flow in a network graph, which represents the maximum amount of flow that can be sent from a designated source to a designated sink in a network.

The Ford-Fulkerson algorithm relies on the concept of augmentation to incrementally improve the flow. Augmentation involves finding an augmenting path in the residual graph and updating the flow values along that path.

The correct option is "Table, value." In dynamic programming, a table (or array) is used to store intermediate results, and in the coin change problem, it is employed to store the minimum number of coins required for each target value. This dynamic programming approach avoids redundant calculations and improves efficiency.

DFS stands for Depth-First Search. It is an algorithm used for traversing or searching tree or graph data structures. In DFS, the algorithm explores as far as possible along each branch before backtracking.

The top pointer in a stack data structure points to the last element added to the stack. This pointer is crucial for efficient push and pop operations, allowing easy access to the most recently added element, ensuring constant time complexity for these operations.

Beyond standard dynamic programming, Matrix Chain Multiplication can be optimized by implementing parallelization techniques for matrix multiplication. This involves efficiently utilizing multiple processors or cores to perform matrix multiplications concurrently, leading to improved performance.

A* search may perform poorly in cases of diverging heuristics where the heuristic estimates significantly deviate from the actual costs. This divergence can lead the algorithm to explore less promising paths, affecting its efficiency and potentially causing it to find suboptimal solutions in certain scenarios.

To optimize selection sort, one can implement a Min Heap that avoids unnecessary swaps. By maintaining a Min Heap, the algorithm can efficiently identify the minimum element without the need for swapping until the final selection.

The basic operations used in calculating the Edit Distance between two strings are Insertion, Substitution, and Deletion. Insertion involves adding a character to one of the strings, Substitution involves replacing a character, and Deletion involves removing a character. These operations collectively measure the minimum number of edits needed to make two strings identical.