BFS guarantees the shortest path in an unweighted graph by using a queue to process nodes in the order they are discovered. Since BFS explores neighbors level by level, the first occurrence of the destination node will yield the shortest path.

Topological sorting is applicable to graphs with unweighted edges. The algorithm relies on the absence of cycles, and introducing weights does not impact the sorting order. However, the weights themselves are not considered in the topological sorting process.

Binary search performs best on sorted data structures. The algorithm relies on the ability to efficiently divide the search space, which is possible when the elements are in a sorted order.

The naive pattern matching algorithm may become inefficient for large texts or patterns due to its quadratic (O(n^2)) time complexity. This is because, in the worst case, the algorithm checks all possible alignments of the pattern with the text, leading to a time-consuming process for large inputs.

Elements are typically added to a queue at the end. This operation is known as "enqueue," and it follows the FIFO principle, ensuring that the element added first is the first to be removed.

The choice of heuristic function significantly impacts the performance of the A* search algorithm. A well-designed heuristic can guide the algorithm efficiently towards the goal, reducing the search space. On the other hand, a poorly chosen heuristic may lead to suboptimal or inefficient paths, affecting the algorithm's overall performance.

Advanced techniques like Dynamic Programming are employed in some regular expression engines to improve matching efficiency. Dynamic Programming can be used to avoid redundant computations, optimizing the overall matching process.

The commonly used data structure to perform a linear search is an array. In a linear search, each element in the array is checked one by one until the target element is found or the entire array is traversed. Arrays provide constant-time access to elements based on their index, making them suitable for linear search operations.

The time complexity of the dynamic programming approach for finding the longest common subsequence is O(n^2), where 'n' is the length of the input sequences. This is achieved through a table-based approach that calculates the length of the LCS for all possible pairs of prefixes of the input sequences.

Dynamic Programming is commonly used to solve the Knapsack Problem efficiently. This approach breaks down the problem into smaller subproblems and stores the solutions to these subproblems, enabling optimal solutions to be computed without redundant calculations.