Difference Between Greedy Best First Search and Hill Climbing Algorithm
Last Updated :
21 Mar, 2024
AI algorithms, or Artificial Intelligence algorithms, are computational procedures or methods designed to solve problems and perform tasks that would typically require human intelligence. These algorithms are fundamental components of artificial intelligence systems and play a crucial role in various applications across different domains.
Pre-requisites: Greedy Best-First Search and Hill Climbing Algorithm
Greedy Best-First Search is a heuristic search method that investigates a search space by choosing the next node that is most near the desired state, depending on a heuristic function.
The heuristic function, which calculates the distance between the current node and the goal, is used by the algorithm to evaluate the accessible nodes at each stage of the search. The next node to be explored is the one with the shortest calculated distance. The AI algorithms keeps going until it either succeeds in reaching the objective node or realizes that it cannot.
As Greedy Best-First Search employs additional information about the issue area to direct its search, it is an educated search algorithm. Unfortunately, it might not always come up with the best option because it considers the distance to the destination and ignores the cost of traveling to the current node.
Overall, Greedy Best-First Search is a fast and efficient algorithm that can be useful in a wide range of applications, particularly in situations where finding a good solution quickly is more important than finding the optimal solution.
An optimization problem-solving heuristic search algorithm is called "hill climbing." By iteratively moving to an adjacent solution with a higher or lower value of the objective function, respectively, the algorithm seeks to discover the maximum or minimum of a given objective function.
The method analyses the current solution and creates a set of surrounding solutions by making minor adjustments to the current solution at each iteration. The algorithm then chooses and moves to the neighbor with the highest (or lowest) objective function value. Until a stopping requirement is satisfied or no better neighbor can be discovered, this process is repeated.
A speedy convergence to a local optimum is possible using the straightforward and effective hill climbing method. However, if the objective function is complex or contains several optima, it may become trapped in local optima and fail to discover the global optimum. Numerous adjustments to the fundamental formula have been created to address this problem and enhance the performance of the method, including random restarts and simulated annealing.
Hill Climbing is a popular optimization approach utilized in many disciplines, including engineering, operations research, and machine learning.
Difference Between Greedy Best-First Search and Hill Climbing Algorithm
| Properties | Greedy Best First Search | Hill Climbing Algorithm
|
|---|
| Definition | A search algorithm that does not take into account the full search space but instead employs heuristics to choose the best route to a goal node. | An approach to searching that skips the full search space and instead chooses the best path to a goal node using heuristics. |
| Goal | To always choose the path with the lowest heuristic cost in order to reach the objective node as rapidly as feasible. | to discover the highest point in the search space, even if it is not the global maximum, in order to optimize a solution. |
| Type | informed search algorithm. | Informed search algorithm.
|
| Heuristics | It estimates the cost of getting to the target node using heuristics. | It evaluates nearby solutions using heuristics. |
| Memory | It doesn't have to keep track of prior nodes. | Only keeps track of the most recent and effective solutions. |
| Completeness | Not guaranteed to find a solution. | Not always possible to locate the global maximum. |
| Efficiency | With a suitable heuristic, it is possible to locate a solution quickly in a wide search space. | It can be effective in locating a local maximum, but it can become trapped in a local optimum. |
| Search space | It uses a breadth-first approach to investigating the search space. | It uses a depth-first approach to investigate the search space. |
| Backtracking | Does not require backtracking. | It can backtrack steps if a better answer cannot be found. |
| Examples | It is used in situations involving pathfinding and graph traversal. | It used in scheduling and logistics optimization problems. |
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