Answers.Body: <p>In a genetic algorithm, crossover (recombination) is the analogy to mating in the real world. For example, you have some genetic information from each parent. In the case of an optimization where you have vectors of features (design variables), you could represent it as vector 1 and 2. Imagine each vector has 10 values. You grab the first 5 from vector 1, and the second 5 from vector 2, and combine those into a new vector which is your "offspring". Note that there are many different ways to do recombination.</p>
  
  <p>When you are deciding on parents to use for recombination, you want to keep some of them the same. Recombination is basically hoping that combining attributes will give you a more optimal set. However, sometimes, you might want to make some of the offspring be copies of the parents in the next generation. So, as <a href="http://www.obitko.com/tutorials/genetic-algorithms/parameters.php" rel="noreferrer">this link</a> explains, the crossover probability determines what percentage of offspring experience recombination. It is probabalistic because there is an element of chance involved in whether or not given offspring experience recombination.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-11-16T19:34:14.943Z
Answers.LastActivityDate: 2017-11-16T19:34:14.943Z
Answers.UserDisplayName: pshlady
Body: <p>I'm reading the A. E. Eiben and J. E. Smith book <em><a href="https://rads.stackoverflow.com/amzn/click/com/3540401849" rel="nofollow noreferrer" rel="nofollow noreferrer">Introduction to Evolutionary Computing</a></em> (Springer 2003).</p>
  
  <p>On section 3.5 Recombination, page 47, the second paragraph said:</p>
  
  <blockquote>
    <p>Recombination operators are usually applied probabilistically according to a <strong>crossover rate</strong> p sub c.</p>
  </blockquote>
  
  <p>What does probabilistically means here?</p>
  
  <p>I think it means that there are a probability that the operator is going to be applied or not.</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-11-16T19:14:37.503Z
Comments.Text: Probabilistic approaches enable variation and uncertainty to be quantified, mainly by using distributions instead of fixed values in risk assessment. A distribution describes the range of possible values (e.g. for toxicity), and shows which values within the range are most likely. (p.s copied from google)
Comments.UserDisplayName: syam
Tags: <genetic-algorithms><terminology><math>
Title: What does "probabilistically" mean?
UserDisplayName: VansFannel
ThreadText: # What does "probabilistically" mean?
  
  (Asked by: username_0 on 2017-11-16)
  
  I'm reading the A. E. Eiben and J. E. Smith book *[Introduction to Evolutionary Computing](https://rads.stackoverflow.com/amzn/click/com/3540401849)* (Springer 2003).
  
  On section 3.5 Recombination, page 47, the second paragraph said:
  
  > Recombination operators are usually applied probabilistically according to a **crossover rate** p sub c.
  
  What does probabilistically means here?
  
  I think it means that there are a probability that the operator is going to be applied or not.
  
  **Tags:** genetic-algorithms, terminology, math
  
  > **Comments:**
  > **username_1**: Probabilistic approaches enable variation and uncertainty to be quantified, mainly by using distributions instead of fixed values in risk assessment. A distribution describes the range of possible values (e.g. for toxicity), and shows which values within the range are most likely. (p.s copied from google)  
  
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  ## 1 answers
  
  ### Answer by username_2 on 2017-11-16. Score: 5 | Accepted answer
  
  In a genetic algorithm, crossover (recombination) is the analogy to mating in the real world. For example, you have some genetic information from each parent. In the case of an optimization where you have vectors of features (design variables), you could represent it as vector 1 and 2. Imagine each vector has 10 values. You grab the first 5 from vector 1, and the second 5 from vector 2, and combine those into a new vector which is your "offspring". Note that there are many different ways to do recombination.
  
  When you are deciding on parents to use for recombination, you want to keep some of them the same. Recombination is basically hoping that combining attributes will give you a more optimal set. However, sometimes, you might want to make some of the offspring be copies of the parents in the next generation. So, as [this link](http://www.obitko.com/tutorials/genetic-algorithms/parameters.php) explains, the crossover probability determines what percentage of offspring experience recombination. It is probabalistic because there is an element of chance involved in whether or not given offspring experience recombination.
  
  
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