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Why does simulated annealing accept some downhill moves?

  • A.It allows the search to escape from local maxima
  • B.Downhill moves are faster to evaluate than uphill ones
  • C.It guarantees the search will terminate in finite time
  • D.It reduces the memory the algorithm needs to store

Answer: A

A strict climber cannot leave a local maximum, whereas one that occasionally goes downhill may cross a valley to a higher peak, and the falling temperature ensures it eventually settles. Evaluation cost, termination and memory are unaffected. Deliberately accepting a worse state, and controlling how often, converts a greedy procedure into a global one, which is a surprisingly general trick.

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