<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>

CC BY-SA 4.0
2022-11-01T12:44:03.320Z
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?
CTMA
CC BY-SA 4.0
2022-10-29T17:03:29.827Z
2022-10-29T17:46:19.860Z
2022-10-29T17:46:19.860Z
Snehal Patel
<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>

<genetic-algorithms><heuristics><genetic-programming><genetic-operators>
how to apply crossover and mutation rates in genetic algorithm?
CTMA
# 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


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## 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?  

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