Which algorithm is known for combining both actual cost and estimated cost in its process?

The A* Search algorithm is renowned for its unique capability to combine both actual cost and estimated cost in its pathfinding process. This is achieved through the use of a cost function that incorporates two key components: the actual cost from the starting point to the current node (often referred to as g(n)) and the estimated cost from the current node to the goal (denoted as h(n)). The total cost function used in A* Search is denoted as f(n) = g(n) + h(n).

This approach allows A* to efficiently navigate toward a goal by dynamically updating its knowledge of the least-cost path while also considering how close a node is to the target. By using both metrics, A* effectively balances exploration and exploitation, making it a powerful algorithm for finding optimal paths in a variety of scenarios, such as graphs and maps.

In contrast, Depth-First Search and Breadth-First Search primarily focus on traversing or searching through a graph without accounting for costs; they do not employ cost functions. The Greedy Algorithm, while it does consider cost, only looks at the estimated cost (h(n)) to make decisions, thus missing the crucial factor of the actual cost accumulated to reach the node. This distinction is what sets