4 votes
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What is the difference between Stochastic Hill Climbing and Simulated Annealing?

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 ...
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4 votes
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What is an objective function?

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 ...
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4 votes
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What is the difference between local search and global search algorithms?

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 ...
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3 votes

Are there local search algorithms that make use of memory to give better solutions?

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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3 votes

Are there local search algorithms that make use of memory to give better solutions?

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 ...
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2 votes

Why does the hill climbing algorithm only produce a local maximum?

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 ...
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1 vote

Why is a mix of greedy and random usually "best" for stochastic local search?

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 ...
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