Answers.Body: <p>Yes, it is possible (depending on the nature of your problem), using the four types of standard NEAT mutation, but it is improbable.</p>
  
  <p>When the NEAT algorithm begins, it operates on a blank canvas. After each generation, the algorithm will either:</p>
  
  <ol>
  <li><p>Construct a new axon</p></li>
  <li><p>Construct a new node on an existing axon</p></li>
  <li><p>Update existing weights/bias</p></li>
  <li><p>Remove a node or axon from the network</p></li>
  </ol>
  
  <p>(However, in general, NEAT does not produce neural networks where two input (or output) nodes are connected.)</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-19T17:29:18.110Z
Answers.Text: Thank you I wanted to ask what you put in the edit section of your answer. But can we say, "outputs and inputs are nodes like other Neurons"? They don't have activation functions I believe, but two or more middle neurons can be connected to one output, no?
Answers.UserDisplayName: Meric Ozcan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-19T23:28:17.077Z
Answers.Text: input nodes are just numbers that are inputted into the system, and do not have activation functions because they are only processing one number. They do, however, feed the input they received to all of the nodes on the next layer through a series of weighted axons. Those weights are processed by the layer receiving the inputs. The output neurons have activation functions because they combine the output of all the nodes in the middle layer into one output for the system. All the middle neurons are always connected to all the outputs (generally).
Answers.UserDisplayName: iamPres
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-20T10:09:54.557Z
Answers.Text: Thanks, what you mean by; All the middle neurons are always connected to all the outputs (generally). I know it happens when you are working with Deep Neural Networks. Does this happen in Neat ? Isnt it random process?
Answers.UserDisplayName: Meric Ozcan
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-20T17:49:51.907Z
Answers.Text: Yes. In neat the process is random and nothing needs to happen.
Answers.UserDisplayName: iamPres
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-01-18T20:37:18.423Z
Answers.LastActivityDate: 2019-10-13T02:03:43.383Z
Answers.LastEditDate: 2019-10-13T02:03:43.383Z
Answers.UserDisplayName: iamPres
Body: <p>Can NEAT produce neural networks where inputs are directly (without intermediate hidden neurons) connected to outputs?</p>
  
Tags: <neural-networks><evolutionary-algorithms><neat><neuroevolution>
Title: Can NEAT produce neural networks where inputs are directly connected to outputs?
UserDisplayName: Meric Ozcan
ThreadText: # Can NEAT produce neural networks where inputs are directly connected to outputs?
  
  (Asked by: username_0 on 2019-01-17)
  
  Can NEAT produce neural networks where inputs are directly (without intermediate hidden neurons) connected to outputs?
  
  **Tags:** neural-networks, evolutionary-algorithms, neat, neuroevolution
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-01-18. Score: 0 | Accepted answer
  
  Yes, it is possible (depending on the nature of your problem), using the four types of standard NEAT mutation, but it is improbable.
  
  When the NEAT algorithm begins, it operates on a blank canvas. After each generation, the algorithm will either:
  
  1. Construct a new axon
  2. Construct a new node on an existing axon
  3. Update existing weights/bias
  4. Remove a node or axon from the network
  
  (However, in general, NEAT does not produce neural networks where two input (or output) nodes are connected.)
  
  > **Comments:**
  > **username_0**: Thank you I wanted to ask what you put in the edit section of your answer. But can we say, "outputs and inputs are nodes like other Neurons"? They don't have activation functions I believe, but two or more middle neurons can be connected to one output, no?  
  > **username_1**: input nodes are just numbers that are inputted into the system, and do not have activation functions because they are only processing one number. They do, however, feed the input they received to all of the nodes on the next layer through a series of weighted axons. Those weights are processed by the layer receiving the inputs. The output neurons have activation functions because they combine the output of all the nodes in the middle layer into one output for the system. All the middle neurons are always connected to all the outputs (generally).  
  > **username_0**: Thanks, what you mean by; All the middle neurons are always connected to all the outputs (generally). I know it happens when you are working with Deep Neural Networks. Does this happen in Neat ? Isnt it random process?  
  > **username_1**: Yes. In neat the process is random and nothing needs to happen.  
  
  ---
  
  