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- Local search techniques in AI include12:
- Luus–Jaakola search: searches locally using a uniform distribution and an exponentially decreasing search-range.
- Random optimization: searches locally using a normal distribution.
- Random search: searches locally by sampling a hypersphere surrounding the current position.
- Pattern search: takes steps along the axes of the search-space using exponentially decreasing step sizes.
- Local search is a particular way of solving a CSP. It keeps improving the values until all the constraints are satisfied2.
Learn more:✕This summary was generated using AI based on multiple online sources. To view the original source information, use the "Learn more" links.Several methods exist for performing local search of real-valued search-spaces:
- Luus–Jaakola searches locally using a uniform distribution and an exponentially decreasing search-range.
en.wikipedia.org/wiki/Local_search_(optimization)Local search is a particular way of solving a CSP. It keeps improving the values until all the constraints are satisfied. It iteratively keeps updating the variables until we arrive at the destination. These algorithms modify the value during each step of the process that gets us closer to the goal.www.oreilly.com/library/view/artificial-intelligence-w… - People also ask
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In computer science, local search is a heuristic method for solving computationally hard optimization problems. Local search can be used on problems that can be formulated as finding a solution maximizing a criterion among a number of candidate solutions. Local search algorithms move from solution to solution in the space of candidate solutions (the search space) by applying local changes, until a solution deemed optimal is found or a time bound is elapsed.
Wikipedia · Text under CC-BY-SA license- Estimated Reading Time: 7 mins
WEBLocal search algorithms • Hill-climbing search – Gradient descent in continuous state spaces – Can use, e.g., Newton’s method to find roots • Simulated annealing search • …
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