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nbro
nbro
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What is the difference between Stochastic Hill Climbing vsand Simulated Annealing?

I am reading about local search: Hillhill climbing, and its types, and Simulated Annealingsimulated annealing

oneOne of Hillthe hill climbing versions is "Stochastic Hill"stochastic hill climbing", which has the following definition:

Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state

someSome sources mentioned that it can be used to avoid local optima.

Then I was reading about Simulated Annealingsimulated annealing and its definition:

At every iteration, a random move is chosen. If it improves the situation then the move is accepted, otherwise it is accepted with some probability less than 1

So, what is the main difference between the towtwo approaches? doesDoes the stochastic choose only random (uphill) successor? ifIf it chooses only (uphill-successors), then how does it avoid local optima?

Stochastic Hill Climbing vs Simulated Annealing

I am reading about local search: Hill climbing and its types and Simulated Annealing

one of Hill climbing versions is "Stochastic Hill climbing" which has the following definition:

Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state

some sources mentioned that it can be used to avoid local optima

Then I was reading about Simulated Annealing and its definition:

At every iteration, a random move is chosen. If it improves the situation then the move is accepted, otherwise it is accepted with some probability less than 1

So what is the main difference between the tow approaches? does the stochastic choose only random (uphill) successor? if it chooses only (uphill-successors) then how does it avoid local optima?

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

I am reading about local search: hill climbing, and its types, and simulated annealing

One of the hill climbing versions is "stochastic hill climbing", which has the following definition:

Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state

Some sources mentioned that it can be used to avoid local optima.

Then I was reading about simulated annealing and its definition:

At every iteration, a random move is chosen. If it improves the situation then the move is accepted, otherwise it is accepted with some probability less than 1

So, what is the main difference between the two approaches? Does the stochastic choose only random (uphill) successor? If it chooses only (uphill-successors), then how does it avoid local optima?

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yaminoyuki
yaminoyuki
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Stochastic Hill Climbing vs Simulated Annealing

I am reading about local search: Hill climbing and its types and Simulated Annealing

one of Hill climbing versions is "Stochastic Hill climbing" which has the following definition:

Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state

some sources mentioned that it can be used to avoid local optima

Then I was reading about Simulated Annealing and its definition:

At every iteration, a random move is chosen. If it improves the situation then the move is accepted, otherwise it is accepted with some probability less than 1

So what is the main difference between the tow approaches? does the stochastic choose only random (uphill) successor? if it chooses only (uphill-successors) then how does it avoid local optima?