Bubble sort performs well when the list is presorted or nearly sorted because it requires fewer comparisons to complete. In a nearly sorted list, many elements are already in their correct positions, reducing the number of swaps needed, making the algorithm more efficient in such scenarios.

A doubly linked list contains nodes that have two pointers: one pointing to the next node in the sequence and another pointing to the previous node. This allows for easy traversal in both directions.

In a static array, the size is fixed at compile time, while in a dynamic array, the size can be changed at runtime to accommodate varying data requirements.

Radix sort is generally faster than comparison-based sorting algorithms for sorting small integers because it takes advantage of the fixed-size nature of integers and avoids comparisons.

The Knapsack Problem is distinct as it specifically aims to maximize the total value of selected items within certain constraints, making it a constrained optimization problem. Other optimization problems may have different objectives or constraints.

DFS primarily employs Pre-order Traversal when traversing a graph. In Pre-order Traversal, the algorithm visits the root node, then recursively performs Pre-order Traversal on the left subtree and the right subtree.

In the Longest Increasing Subsequence (LIS) problem, "patience" refers to the ability to split and combine sequences of numbers. The algorithm involves finding the longest increasing subsequence in a given sequence.

The patience sorting algorithm is inspired by the card game Solitaire. In this algorithm, the process of sorting is similar to organizing a deck of cards in the game of Solitaire.

The time complexity of the binary search algorithm on a sorted array is O(log n), where 'n' is the number of elements in the array. This logarithmic time complexity makes binary search highly efficient for large datasets.

The effectiveness of the A* search algorithm heavily depends on the heuristic function, which should be admissible (never overestimates) and consistent. The heuristic guides the search towards the goal efficiently, influencing the algorithm's ability to find the optimal path in various applications.