Answers.Body: <p>To try to accomplish your ANN based evolution watch task, you need established techniques such as <a href="https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies" rel="nofollow noreferrer">NeuroEvolution of Augmenting Topologies (NEAT)</a> to evolve neural networks with structure and parameters optimized <em>simultaneously</em>.</p>
  <blockquote>
  <p>NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures (&quot;complexifying&quot;)... On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods.</p>
  </blockquote>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2024-11-29T21:59:23.333Z
Answers.Text: NEAT is a method of selecting among several neural networks based on their fitness. My method is that survival of the fittest occurs when various logics interfere and interact within a single neural network.
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Answers.CreationDate: 2024-11-29T22:29:14.723Z
Answers.Text: According to connectionism even the so called random and surviving logics ought to be represented by neural networks themselves, it's neurons all the way down and recall the first Pitt's neural network was just to model some kind of human first order binary logics. So in theory NEAT should be applied to watch true evolution especially akin to humans which seems your interest here, and you specifically specified in OP's 3) that "*Periodically mutates the robot's neural network*". Your 'logics' is not a common terminology in contemporary ML.
Answers.UserDisplayName: cinch
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Answers.CreationDate: 2024-11-29T22:43:57.527Z
Answers.Text: My method is,
  1. Periodically mutate "one" neural network
  
  2. Random and diverse logics occur in that "one" neural network
  
  3. Those logics interfere and interact, resulting in survival of the fittest
  
  4. The surviving logics acquire general intelligence to respond to the random logics
  
  5. A robot with a neural network that has general intelligence acquires general intelligence
  6. Observe how a virtual robot interacts with objects in a 3D environment
  
  Sorry for telling the same story, but I am writing it again because you think I am following the NEAT algorithm.
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Answers.CreationDate: 2024-11-29T07:36:05.907Z
Answers.LastActivityDate: 2024-11-29T07:36:05.907Z
Answers.UserDisplayName: cinch
Body: <ol>
  <li><p>A virtual robot with a neural network is placed in a 3D space with various objects.</p>
  </li>
  <li><p>The robot's neural network is connected to the 3D space through input and output.</p>
  </li>
  <li><p>Periodically mutates the robot's neural network.</p>
  </li>
  <li><p>Then, random logics are created in the neural network.</p>
  </li>
  <li><p>The logics interact, survival of the fittest occurs, and evolution occurs.</p>
  </li>
  <li><p>The surviving logics acquire general intelligence to respond to the random logic.</p>
  </li>
  <li><p>Evolution occurs at the speed of the computer (evolution on Earth takes a long time because material movement is slow).</p>
  </li>
  <li><p>The robot acquires general intelligence.</p>
  </li>
  <li><p>Just watch how the robot interacts with objects in the 3D space.</p>
  </li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-29T09:43:26.143Z
Comments.Text: Sentence 5 is doing a lot of heavy lifting here. 1. How do you evaluate the fitness of your "raqndom logics"? 2. How do you evolve them? I think you need to be a lot more specific. Also, what is your question here?
Comments.UserDisplayName: Oliver Mason
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-11-29T21:56:10.713Z
Comments.Text: @OliverMason As logics interfere and interact with each other, survival of the fittest occurs naturally, and thus evolution occurs.
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Comments.CreationDate: 2024-12-02T09:09:31.183Z
Comments.Text: That doesn't answer my question... this is all rather vague and unspecific. How do they interact? How do you decide who is the fittest? How does evolution occur?
Comments.UserDisplayName: Oliver Mason
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-12-21T02:36:20.077Z
Comments.Text: @Displayname - could you make an attempt to edit this question and clarify what *exactly* it is you're asking here? As it stands this question doesn't strike me as useful, given how vague it is.
Comments.UserDisplayName: mindcrime
Tags: <evolutionary-algorithms>
Title: How to watch evolution
UserDisplayName: Display name
ThreadText: # How to watch evolution
  
  (Asked by: username_0 on 2024-11-29)
  
  1. A virtual robot with a neural network is placed in a 3D space with various objects.
  2. The robot's neural network is connected to the 3D space through input and output.
  3. Periodically mutates the robot's neural network.
  4. Then, random logics are created in the neural network.
  5. The logics interact, survival of the fittest occurs, and evolution occurs.
  6. The surviving logics acquire general intelligence to respond to the random logic.
  7. Evolution occurs at the speed of the computer (evolution on Earth takes a long time because material movement is slow).
  8. The robot acquires general intelligence.
  9. Just watch how the robot interacts with objects in the 3D space.
  
  **Tags:** evolutionary-algorithms
  
  > **Comments:**
  > **username_1**: Sentence 5 is doing a lot of heavy lifting here. 1. How do you evaluate the fitness of your "raqndom logics"? 2. How do you evolve them? I think you need to be a lot more specific. Also, what is your question here?  
  > **username_0**: @username_1 As logics interfere and interact with each other, survival of the fittest occurs naturally, and thus evolution occurs.  
  > **username_1**: That doesn't answer my question... this is all rather vague and unspecific. How do they interact? How do you decide who is the fittest? How does evolution occur?  
  > **username_2**: @username_0 - could you make an attempt to edit this question and clarify what *exactly* it is you're asking here? As it stands this question doesn't strike me as useful, given how vague it is.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_3 on 2024-11-29. Score: -1
  
  To try to accomplish your ANN based evolution watch task, you need established techniques such as [NeuroEvolution of Augmenting Topologies (NEAT)](https://en.wikipedia.org/wiki/Neuroevolution_of_augmenting_topologies) to evolve neural networks with structure and parameters optimized *simultaneously*.
  
  > NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying")... On simple control tasks, the NEAT algorithm often arrives at effective networks more quickly than other contemporary neuro-evolutionary techniques and reinforcement learning methods.
  
  > **Comments:**
  > **username_0**: NEAT is a method of selecting among several neural networks based on their fitness. My method is that survival of the fittest occurs when various logics interfere and interact within a single neural network.  
  > **username_3**: According to connectionism even the so called random and surviving logics ought to be represented by neural networks themselves, it's neurons all the way down and recall the first Pitt's neural network was just to model some kind of human first order binary logics. So in theory NEAT should be applied to watch true evolution especially akin to humans which seems your interest here, and you specifically specified in OP's 3) that "*Periodically mutates the robot's neural network*". Your 'logics' is not a common terminology in contemporary ML.  
  > **username_0**: My method is, 1. Periodically mutate "one" neural network  2. Random and diverse logics occur in that "one" neural network  3. Those logics interfere and interact, resulting in survival of the fittest  4. The surviving logics acquire general intelligence to respond to the random logics  5. A robot with a neural network that has general intelligence acquires general intelligence 6. Observe how a virtual robot interacts with objects in a 3D environment  Sorry for telling the same story, but I am writing it again because you think I am following the NEAT algorithm.  
  
  ---
  
  