![]() In this region, the values attained by the neighboring states are the same. Momentum enables the hill-climbing algorithm to take huge steps that will make it move past the local maximum. This technique adds a certain proportion (m) of the initial weight to the current one. This problem can be solved using momentum. This will lead to the hill-climbing process’s termination, even though this is not the best possible solution. The greedy approach feature will not move the algorithm to a worse off state. Local maximumĪt this point, the neighboring states have lower values than the current state. There are three regions in which a hill-climbing algorithm cannot attain a global maximum or the optimal solution: local maximum, ridge, and plateau. Shoulder: This is a plateau whose edge is stretching upwards.Flat local maximum: This is a flat region where the neighboring solutions attain the same value.Current state: This is the existing or present state.Global maximum: This is the best possible solution achieved by the algorithm.Local maximum: A local maximum is a solution that surpasses other neighboring solutions or states but is not the best possible solution.The objective function has been shown on the y-axis, while the state-space represents the x-axis.Ī state-space diagram consists of various regions that can be explained as follows The following diagram shows a simple state-space diagram. More information about local minimum, local maximum, global minimum, and global maximum can be found here. If the cost function represents this axis, we aim to establish the local minimum and global minimum. If the objective function is the y-axis, we aim to establish the local maximum and global maximum. Incremental change: The algorithm improves the current solution by incremental changes.Ī state-space diagram provides a graphical representation of states and the optimization function.The feedback mechanism is enhanced through the generate-and-test technique. Feedback mechanism: The algorithm has a feedback mechanism that helps it decide on the direction of movement (whether up or down the hill).No Backtracking: A hill-climbing algorithm only works on the current state and succeeding states (future).The greedy approach enables the algorithm to establish local maxima or minima. It employs a greedy approach: This means that it moves in a direction in which the cost function is optimized.Features of a hill climbing algorithmĪ hill-climbing algorithm has four main features: The peak state cannot undergo further improvements. This process will continue until a peak solution is achieved. When the current state is improved, the algorithm will perform further incremental changes to the improved state. This explains why the algorithm is termed as a hill-climbing algorithm.Ī hill-climbing algorithm’s objective is to attain an optimal state that is an upgrade of the existing state. The process of continuous improvement of the current state of iteration can be termed as climbing. The heuristic function is used as the basis for this precondition. It begins with a non-optimal state (the hill’s base) and upgrades this state until a certain precondition is met. ![]() ![]() This algorithm has a node that comprises two parts: state and value. This algorithm comes to an end when the peak is reached. Introduction to hill climbing algorithmĪ hill-climbing algorithm is a local search algorithm that moves continuously upward (increasing) until the best solution is attained. The article will also highlight the applications of this algorithm. It discusses various aspects such as features, problems, types, and algorithm steps of the hill-climbing algorithm. This article will improve our understanding of hill climbing in artificial intelligence. This algorithm is used to optimize mathematical problems and in other real-life applications like marketing and job scheduling. A hill-climbing algorithm is an Artificial Intelligence (AI) algorithm that increases in value continuously until it achieves a peak solution. ![]()
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