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Local search algorithms are essential tools in artificial intelligence and optimization, employed to find high-quality solutions in large and complex problem spaces. Key algorithms include Hill-Climbing Search, Simulated Annealing, Local Beam Search, Genetic Algorithms, and Tabu Search.
Describe the advantages of local search over other search algorithms. Formulate a real-world problem as a local search problem. Verify whether a state is a local/global optimum.
Local search. Keep track of single current state. Move only to neighboring states. Ignore paths. Advantages: Use very little memory. Can often find reasonable solutions in large or infinite (continuous) state spaces. “Pure optimization” problems. All states have an objective function.
Local search is a search algorithm that maintains a single node and searches by moving to a neighboring node. This type of algorithm is different from previous types of search that we saw. Whereas in maze solving, for example, we wanted to find the quickest way to the goal, local search is interested in finding the best answer to a question.
A local search algorithm in artificial intelligence is a type of optimization algorithm used to find the best solution to a problem by repeatedly making minor adjustments to an initial solution.
Chapter 12. Local Search. Slides by Kevin Wayne. Copyright @ 2005 Pearson-Addison Wesley. All rights reserved. Coping With NP-Hardness. Q. Suppose I need to solve an NP-hard problem. What should I do? A. Theory says you're unlikely to find poly-time algorithm. Must sacrifice one of three desired features. Solve problem to optimality.
•Describe and implement the following local search algorithms. •Iterative improvement algorithm with min-conflict heuristic for CSPs •Hill Climbing (Greedy Local Search) •Random Walk •Simulated Annealing •Beam Search •Genetic Algorithm.
Local search is a very natural algorithmic technique, and is often combined (in practice) as a post-processing step with other algorithms (e.g., use a greedy algorithm to build up something, then use local search to try to make it better).
Local search Local search. Algorithm that explores the space of possible solutions in sequential fashion, moving from a current solution to a “nearby” one. Neighbor relation. Let S ∼ S ʹ be a neighbor relation for the problem. Gradient descent. Let S denote current solution. If there is a neighbor S ʹ of S
Algorithm 1: Local Search Algorithm for Matching Problem Data: Graph G = (V; E), with jV j = n; jEj = m. Start with any matching M 6= ; while there are edges R to remove and A to add such that: /* local move. */ E, R M, jRj < jAj. and (MnR) [ A is a matching. do. (MnR) [ A.