What is meant by local search algorithm?
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Types of Local Search Algorithms
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Related PostsLocal Search in Artificial Intelligence is an optimizing algorithm to find the optimal solution more quickly. Local search algorithms are used when we care only about a solution but not the path to a solution. Local search is used in most of the models of AI to search for the optimal solution according to the cost function of that model. Local search is used in linear regression, neural networks, clustering models. Hill climbing, simulated annealing, tabu search are some of the local search algorithms. If you wish to pursue a career in AI, then register for Intellipaat’s Artificial Intelligence Course. The informed and uninformed search expands the nodes systematically in two ways:
Which leads to a solution state required to reach the goal node. But beyond these “classical search algorithms," we have some “local search algorithms” where the path cost does not matters, and only focus on solution-state needed to reach the goal node. A local search algorithm completes its task by traversing on a single current node rather than multiple paths and following the neighbors of that node generally. Although local search algorithms are not systematic, still they have the following two advantages:
Does the local search algorithm work for a pure optimized problem? Yes, the local search algorithm works for pure optimized problems. A pure optimization problem is one where all the nodes can give a solution. But the target is to find the best state out of all according to the objective function. Unfortunately, the pure optimization problem fails to find high-quality solutions to reach the goal state from the current state. Note: An objective function is a function whose value is either minimized or maximized in different contexts of the optimization problems. In the case of search algorithms, an objective function can be the path cost for reaching the goal node, etc. Working of a Local search algorithmLet's understand the working of a local search algorithm with the help of an example: Consider the below state-space landscape having both:
The local search algorithm explores the above landscape by finding the following two points:
We will understand the working of these points better in Hill-climbing search. Below are some different types of local searches:
We will discuss above searches in the next section. Note: Local search algorithms do not burden to remember all the nodes in the memory; it operates on complete state-formulation.
What are local search algorithm in AI?Hill climbing algorithm is a local search algorithm which continuously moves in the direction of increasing elevation/value to find the peak of the mountain or best solution to the problem. It terminates when it reaches a peak value where no neighbor has a higher value.
What is meant by local search?Local search is the use of specialized Internet search engines that allow users to submit geographically constrained searches against a structured database of local business listings.
What are the various local search algorithms?Simulated Annealing (SA) is probabilistic and an optimization technique, and it helps to find out the global optimum for a given function. The local beam search algorithm begins with randomly generated states and keeps track of them to find out the best until it achieves a goal.
What is global search and local search?Local search: For narrow problems where the global solution is required. Global search: For broad problems where the global optima might be intractable.
What is local beam search algorithm explain with an example?In the context of a local search, we call local beam search a specific algorithm that begins selecting β generated states. Then, for each level of the search tree, it always considers β new states among all the possible successors of the current ones until it reaches a goal.
What is meant by search algorithm?In computer science, a search algorithm is an algorithm (if more than one, algorithms) designed to solve a search problem. Search algorithms work to retrieve information stored within particular data structure, or calculated in the search space of a problem domain, with either discrete or continuous values.
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