5 votes
Accepted

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 ...
nbro's user avatar
  • 40.2k
4 votes

When should I use simulated annealing as opposed to a genetic algorithm?

Simulated Annealing vs genetic algorithm? Simulated annealing is a materials science analogy and involves the introduction of noise to avoid search failure due to local minima. See images below. To ...
Douglas Daseeco's user avatar
3 votes
Accepted

How is simulated annealing better than hill climbing methods?

In the least technical, most intuitive way possible: Simulated Annealing can be considered as a modification of Hill Climbing (or Hill Descent). Hill Climbing/Descent attempts to reach an optimum ...
Abdul Rahman Dabbour's user avatar
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.
Matthew Gray's user avatar
  • 4,262
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 ...
BobbyPi's user avatar
  • 227
2 votes

What are examples of daily life applications that use simulated annealing?

Examples of simulated annealing in the 2010s These are a few examples. Optimised simulated annealing for Ising spin glasses, 2015, S.V. Isakov et. al. A parallel simulated annealing method for the ...
Douglas Daseeco's user avatar
2 votes

What are most commons methods to measure improvement rate in a meta-heuristic?

You can use one of your suggested methods to calculate the relative improvement, but you need also to define a threshold value $\epsilon$ that determines when a relative improvement is negligible, and ...
nbro's user avatar
  • 40.2k
1 vote

Why does Simulated Annealing not take worse solution if the energy difference becomes higher?

Note that you can't really predict whether your escape from a local minimum will work or not - you might just wind up in another, worse local minimum. The probability function you describe increases ...
Nuclear Hoagie's user avatar
1 vote
Accepted

What is the difference between simulated annealing and deterministic annealing?

After diving deeper into the material I am able to answer my own question: Simulated Annealing tries to optimize a energy (cost) function by stochastically searching for minima at different ...
Tinu's user avatar
  • 628
1 vote

What are examples of daily life applications that use simulated annealing?

Simulated annealing is just one of the approaches for an optimization problem: Given a function f(X), you want to find an X where f(X) is optimal (has maximum or minimum value). Shaking (a ...
dzieciou's user avatar
  • 113

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