Difference Between Greedy Best First Search and Hill Climbing Algorithm

Introduction:

Greedy Best First Search (GBFS) is a search algorithm that uses a heuristic to select the most promising node, with the purpose of reaching the goal as rapidly as possible but not always finding the ideal solution. In contrast, the Hill Climbing Algorithm iteratively improves a solution by making minor changes, always going towards higher-valued neighbours, but it might become stuck in local optima and lacks systematic exploration.

What is Greedy-Best-First search algorithm?

`Greedy Best-First Search is an AI search algorithm that seeks the most promising path from a given beginning point to a destination. The algorithm focuse­s on the paths that seem most promising, e­ven if they are not the­ shortest. It works by calculating the cost of each pote­ntial path and then expanding the one­ with the lowest cost. This process is re­peated until the targe­t is reached`

The algorithm use­s a heuristic function to determine­ which path is more promising. The heuristic function conside­rs both the cost of the current path and the­ predicted cost of the re­maining paths. If the cost of the prese­nt path is less than the predicte­d cost of the other alternative­s, it is chosen. This process continues until the­ target is met.

How does Greedy Best-First Search Work?

1. Greedy Best-First Search determines the cost of each feasible path and then expands the path with the lowest cost. This process is repeated until the target is reached.
2. The algorithm employs a heuristic function to assess which path is more promising.
3. The heuristic function considers both the cost of the current path and the predicted cost of the remaining paths.
1. If the cost of the present path is less than the predicted cost of the other alternatives, it is picked.
4. This method is repeated until the target is met.

Hill Climbing

Hill Climbing is a technique for solving specific optimization problems. In this strategy, we begin with a suboptimal answer and repeatedly improve it until a condition is maximized.

Starting with a suboptimal solution is analogous to starting at the bottom of a hill, improving the solution is analogous to walking up the hill, and maximizing some condition is analogous to reaching the top of the hill.

Therefore, the hill climbing strategy can be understood as the following phases:

`Constructing a suboptimal solution that obeys the constraints of the problem.Improving the solution step by step.Improving the solution until no further improvements are achievable.`

The Hill Climbing approach is mostly used for addressing computationally difficult issues. It only considers the current and immediate future states. As a result, this strategy is memory efficient because it does not use a search tree.

Advantages of the Hill Climbing algorithm

• Hill Climbing is a simple & easy-to-implement algorithm.
• It can be applied to a wide range of optimization problems, even with great search spaces and constraints that are not linear.
• Usually, it is quite efficient in finding local optima and hence it’s an excellent choice for problems requiring quick solutions.
• The method is highly extensible as well as updatable with respect to including new heuristics or limitations.

Disadvantages of the Hill Climbing algorithm

• This can lead to getting stuck into local optima so that global optimal solution cannot be found by this technique.
• This method requires a good initial solution otherwise it may end up with no or low-quality solutions at all.
• Moreover, it does not fully explore the search space which may limit its ability to find better results than what other algorithms can discover.
• Some applications might benefit more from other optimization techniques like genetic algorithms or simulated annealing than hill climbing technique does.

Conclusion

While both Greedy Best First Search and Hill Climbing Algorithm seek to optimize solutions via heuristic evaluations, their search methodologies differ. Greedy Best First Search prioritizes nodes based entirely on heuristic values, potentially neglecting better alternatives, whereas the Hill Climbing Algorithm continually refines solutions by locally maximizing heuristic values but may become trapped in suboptimal results.