Answers.Body: <p>The remaining will be models selected from the original population based on the best performers (or some other user defined criteria).  You don't do anything to those models - no crossover and no mutations - just keep them as they are.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-11-01T12:44:03.320Z
Answers.Text: so first of all we gonna select 10% with any selected method, after that, we gonna fill the remaining 50% of the next generation with the old individuals' best performers; if we sum this we gonna find 60% of the last individuals will be added to the next generation?
Answers.UserDisplayName: CTMA
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-10-29T17:03:29.827Z
Answers.LastActivityDate: 2022-10-29T17:46:19.860Z
Answers.LastEditDate: 2022-10-29T17:46:19.860Z
Answers.UserDisplayName: Snehal Patel
Body: <p>I'm working with genetic programming and let's say I have the following operator:</p>
  <ul>
  <li>pop_size = 100</li>
  <li>Crossover ratio = 0.4</li>
  <li>Mutation Ratio = 0.2</li>
  <li>Selection Ratio = 0.1</li>
  </ul>
  <p>What is exactly the next generation will look like?</p>
  <ul>
  <li>Select 10% from the last pop with any selection scheme.</li>
  <li>Fill 40% of the next generation with the crossover between parents.</li>
  <li>Mutate the new offspring based on that mutation ratio before adding it to the next generation.</li>
  <li>That will be 50% of the next generation filled up, and we have to fill the remaining 50%, how to do this? by mutating the individuals from the last generation according to the mutation ratio? add some random genes to the next generation?</li>
  </ul>
  
Tags: <genetic-algorithms><heuristics><genetic-programming><genetic-operators>
Title: how to apply crossover and mutation rates in genetic algorithm?
UserDisplayName: CTMA
ThreadText: # how to apply crossover and mutation rates in genetic algorithm?
  
  (Asked by: username_0 on 2022-10-29)
  
  I'm working with genetic programming and let's say I have the following operator:
  
  * pop\_size = 100
  * Crossover ratio = 0.4
  * Mutation Ratio = 0.2
  * Selection Ratio = 0.1
  
  What is exactly the next generation will look like?
  
  * Select 10% from the last pop with any selection scheme.
  * Fill 40% of the next generation with the crossover between parents.
  * Mutate the new offspring based on that mutation ratio before adding it to the next generation.
  * That will be 50% of the next generation filled up, and we have to fill the remaining 50%, how to do this? by mutating the individuals from the last generation according to the mutation ratio? add some random genes to the next generation?
  
  **Tags:** genetic-algorithms, heuristics, genetic-programming, genetic-operators
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2022-10-29. Score: 1
  
  The remaining will be models selected from the original population based on the best performers (or some other user defined criteria). You don't do anything to those models - no crossover and no mutations - just keep them as they are.
  
  > **Comments:**
  > **username_0**: so first of all we gonna select 10% with any selected method, after that, we gonna fill the remaining 50% of the next generation with the old individuals' best performers; if we sum this we gonna find 60% of the last individuals will be added to the next generation?  
  
  ---
  
  