Answers.Body: <p>In general, hill climbing algorithms select a random initial solution, then takes the best move available after evaluating all possible operations available. The possible operations are determined by the search operators (which you determine and is dependent on the problem setting). So by your words, you should 'replace it with the fittest solution among its neighbours'.</p>
  
  <p>Mutation can be applied as a search operator, but this varies by degree and context. For example, in structure learning for bayesian networks, a random sequence of search operators are sometimes applied to escape local optima during the search process.</p>
  
  <p>See <a href="https://www.cs.helsinki.fi/u/bmmalone/probabilistic-models-spring-2014/StructureLearning.pdf" rel="nofollow noreferrer">https://www.cs.helsinki.fi/u/bmmalone/probabilistic-models-spring-2014/StructureLearning.pdf</a> - slide 9 for random restarts</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-01-19T17:28:01.783Z
Answers.LastActivityDate: 2018-01-19T17:35:09.140Z
Answers.LastEditDate: 2018-01-19T17:35:09.140Z
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Body: <p>As far as I understand, the hill climbing algorithm is a local search algorithm that selects any random solution as an initial solution to start the search. Then, should we apply an operation (i.e., mutation) on the selected solution to get a new one or we replace it with the fittest solution among its neighbours? </p>
  
  <p>This is the part of the algorithm where I am confused. </p>
  
Tags: <search><optimization><hill-climbing>
Title: Should the mutation be applied with the hill climbing algorithm?
UserDisplayName: Nasser
ThreadText: # Should the mutation be applied with the hill climbing algorithm?
  
  (Asked by: username_0 on 2018-01-19)
  
  As far as I understand, the hill climbing algorithm is a local search algorithm that selects any random solution as an initial solution to start the search. Then, should we apply an operation (i.e., mutation) on the selected solution to get a new one or we replace it with the fittest solution among its neighbours?
  
  This is the part of the algorithm where I am confused.
  
  **Tags:** search, optimization, hill-climbing
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-01-19. Score: 1
  
  In general, hill climbing algorithms select a random initial solution, then takes the best move available after evaluating all possible operations available. The possible operations are determined by the search operators (which you determine and is dependent on the problem setting). So by your words, you should 'replace it with the fittest solution among its neighbours'.
  
  Mutation can be applied as a search operator, but this varies by degree and context. For example, in structure learning for bayesian networks, a random sequence of search operators are sometimes applied to escape local optima during the search process.
  
  See <https://www.cs.helsinki.fi/u/bmmalone/probabilistic-models-spring-2014/StructureLearning.pdf> - slide 9 for random restarts
  
  
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