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Russell and Norvig's book (3rd edition) describe these two algorithms (section 4.1.1., p. 122) and this book is the reference that you should generally use when studying search algorithms in artificial intelligence. I am familiar with simulated annealing (SA), given that I implemented it in the past to solve a combinatorial problem, but I am not very ...


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The difference between a local search algorithm (like beam search) and a complete search algorithm (like A*) is, for the most part, small. Local search algorithms will not always find the correct or optimal solution, if one exists. For example, with beam search (excluding an infinite beam width), it sacrifices completeness for greater efficiency by ordering ...


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Tabu search uses memory to rule out parts of the neighborhood for local search, allowing the trajectory to typically pass through local optima instead of getting stuck in them.


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You could parallelize the search by dividing the global space in distinct regions/subsets. Then apply in each region a local search. This way you can search the global space systematically, more exhaustively and perhaps in different ways (e.g by applying a different local search method to each region). Finally you can compare the results and choose the best ...


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The "objective function" is the function that you want to minimise or maximise in your problem. The expression "objective function" is used in several different contexts (e.g. machine learning or linear programming), but it always refers to the function to be maximised or minimised in the specific (optimisation) problem. Hence, this expression is used in ...


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This part of your sentence is not always true "and not reached to final goal/solution". If you have just one maximum at all and it is finite, hill climbing (HL) can reach to it and it is a global maximum too (for example, if the function is a parabola). To answer your question is back to the stop criteria of HL. It will be stopped when reaching to a maximum ...


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This question really looks like a homework problem, in part because it is too vague (what does it mean to 'get stuck' exactly?). Hill climbing stops when it reaches a local maximum. Hill climbing is an uninformed search algorithm, so it does not make use of a heuristic. Hill climbing may or may not stop on a ridge, depending on the implementation. Some ...


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As an example of local/global minima, imagine being on a rugged, mountainous landscape, and you want to find the lowest point within some area. For a greedy search, every step you take will take you downhill. If you go downhill long enough, you'll eventually find a flat spot, which is a minimum - from here, there's no step you can take that will get you any ...


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