<p>Perturbed here means adding a small random value to the weight. That random value comes from a uniform distribution or from a gaussian (or any distribution really). 
Imagine just nudging the weight by a little. </p>

<p>It’s done to overcome the problem of local minima where models can get stuck with a good set of weights but not the best set of weights. By perturbing the weights a little, the model has a chance of finding a better set of weights. Searching for methods that deal with local minima like stochastic gradient descent will give you a better intuition. </p>

CC BY-SA 3.0
2018-03-06T22:40:37.427Z
2018-03-07T16:19:27.323Z
2018-03-07T16:19:27.323Z
Jaden Travnik
<p>I am reading through the NEAT paper <a href="http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf" rel="nofollow noreferrer">here</a>. On page 14 of the PDF, there is this quote about mutation:</p>

<blockquote>
  <p>There was an 80% chance of a genome having its connection weights mutated, in which case <strong>each weight had a 90% chance of being uniformly perturbed</strong> and a 10% chance of being assigned a new random value.</p>
</blockquote>

<p>What exactly does it mean to perturb weights? What is uniform vs. nonuniform perturbation?</p>

<p>Is there an established method to do this? I am imagining the process as multiplying each connection weight by a random number, but I'm unfamiliar with the term.</p>

<neural-networks><evolutionary-algorithms><neat>
How are connection weights "perturbed"?
natedogg
# How are connection weights "perturbed"?

(Asked by: username_0 on 2018-03-06)

I am reading through the NEAT paper [here](http://nn.cs.utexas.edu/downloads/papers/stanley.ec02.pdf). On page 14 of the PDF, there is this quote about mutation:

> There was an 80% chance of a genome having its connection weights mutated, in which case **each weight had a 90% chance of being uniformly perturbed** and a 10% chance of being assigned a new random value.

What exactly does it mean to perturb weights? What is uniform vs. nonuniform perturbation?

Is there an established method to do this? I am imagining the process as multiplying each connection weight by a random number, but I'm unfamiliar with the term.

**Tags:** neural-networks, evolutionary-algorithms, neat


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## 1 answers

### Answer by username_1 on 2018-03-06. Score: 4 | Accepted answer

Perturbed here means adding a small random value to the weight. That random value comes from a uniform distribution or from a gaussian (or any distribution really).
Imagine just nudging the weight by a little.

It’s done to overcome the problem of local minima where models can get stuck with a good set of weights but not the best set of weights. By perturbing the weights a little, the model has a chance of finding a better set of weights. Searching for methods that deal with local minima like stochastic gradient descent will give you a better intuition.


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