April 29, 2022 - Reading time: 4 minutes
A hill-climbing algorithm is an Artificial Intelligence (AI) algorithm that increases in value continuously until it achieves a peak solution. This algorithm is used to optimize mathematical problems and in other real-life applications like marketing and job scheduling.
A hill-climbing algorithm is a local search algorithm that moves continuously upward (increasing) until the best solution is attained. This algorithm comes to an end when the peak is reached.
This algorithm has a node that comprises two parts: state and value. It begins with a non-optimal state (the hill’s base) and upgrades this state until a certain precondition is met. The heuristic function is used as the basis for this precondition. The process of continuous improvement of the current state of iteration can be termed as climbing. This explains why the algorithm is termed as a hill-climbing algorithm.
A hill-climbing algorithm’s objective is to attain an optimal state that is an upgrade of the existing state. When the current state is improved, the algorithm will perform further incremental changes to the improved state. This process will continue until a peak solution is achieved. The peak state cannot undergo further improvements.
A hill-climbing algorithm has four main features:
It employs a greedy approach: This means that it moves in a direction in which the cost function is optimized. The greedy approach enables the algorithm to establish local maxima or minima.
No Backtracking: A hill-climbing algorithm only works on the current state and succeeding states (future). It does not look at the previous states.
Feedback mechanism: The algorithm has a feedback mechanism that helps it decide on the direction of movement (whether up or down the hill). The feedback mechanism is enhanced through the generate-and-test technique.
Incremental change: The algorithm improves the current solution by incremental changes.
The following are the types of a hill-climbing algorithm:
This is a simple form of hill climbing that evaluates the neighboring solutions. If the next neighbor state has a higher value than the current state, the algorithm will move. The neighboring state will then be set as the current one.
This algorithm consumes less time and requires little computational power. However, the solutions produced by the algorithm are sub-optimal. In some cases, an optimal solution may not be guaranteed.
Conduct an assessment of the current state. Stop the process and indicate success if it is a goal state. Perform looping on the current state if the assessment in step 1 did not establish a goal state. Continue looping to attain a new solution. Assess the new solution. If the new state has a higher value than the current state in steps 1 and 2, then mark it as a current state. Continue steps 1 to 4 until a goal state is attained. If this is the case, then exit the process.
This algorithm is more advanced than the simple hill-climbing algorithm. It chooses the next node by assessing the neighboring nodes. The algorithm moves to the node that is closest to the optimal or goal state.
Conduct an assessment of the current state. Stop the process and indicate success if it is a goal state. Perform looping on the current state if the assessment in step 1 did not establish a goal state. Continue looping to attain a new solution. Establish or set a state (X) such that current state successors have higher values than it. Run the new operator and produce a new solution. Assess this solution to establish whether it is a goal state. If this is the case, exit the program. Otherwise, compare it with the state (X). If the new state has a higher value than the state (X), set it as X. The current state should be set to Target if the state (X) has a higher value than the current state. Stochastic hill climbing In this algorithm, the neighboring nodes are selected randomly. The selected node is assessed to establish the level of improvement. The algorithm will move to this neighboring node if it has a higher value than the current state.
The hill-climbing algorithm can be applied in the following areas:
Marketing A hill-climbing algorithm can help a marketing manager to develop the best marketing plans. This algorithm is widely used in solving Traveling-Salesman problems. It can help by optimizing the distance covered and improving the travel time of sales team members. The algorithm helps establish the local minima efficiently.
Robotics Hill climbing is useful in the effective operation of robotics. It enhances the coordination of different systems and components in robots.
Job Scheduling The hill climbing algorithm has also been applied in job scheduling. This is a process in which system resources are allocated to different tasks within a computer system. Job scheduling is achieved through the migration of jobs from one node to a neighboring node. A hill-climbing technique helps establish the right migration route.
Hill climbing is a very resourceful technique used in solving huge computational problems. It can help establish the best solution for problems. This technique has the potential of revolutionizing optimization within artificial intelligence.
In the future, technological advancement to the hill climbing technique will solve diverse and unique optimization problems with better advanced features.