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faber_tweann

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Faber TWEANN - Topology and Weight Evolving Artificial Neural Networks

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faber_tweann assets neuroevolution-cycle.svg
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assets/neuroevolution-cycle.svg 

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  <!-- Title -->

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    The Neuroevolution Cycle

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    Population-based optimization through iterative evaluation, selection, and reproduction

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    1. POPULATION

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    Collection of neural network genotypes

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    2. EVALUATION

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    Run phenotype in environment

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    Calculate fitness score

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    3. SELECTION

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    Tournament / Truncation

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    4. REPRODUCTION

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  <text x="100" y="345" font-family="Arial, sans-serif" font-size="8" text-anchor="middle" fill="rgba(255,255,255,0.8)">weights, topology</text>



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    Elitism: best genotypes copied unchanged

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    Fitness Functions

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  <text x="55" y="130" font-family="Arial, sans-serif" font-size="10" fill="#666">XOR accuracy</text>

  <text x="55" y="148" font-family="Arial, sans-serif" font-size="10" fill="#666">Pole balance time</text>

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  <text x="55" y="220" font-family="Arial, sans-serif" font-size="10" fill="#666">Custom evaluators...</text>



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    Mutation Types

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  <text x="715" y="420" font-family="Arial, sans-serif" font-size="10" fill="#666">• Weight perturbation</text>

  <text x="715" y="438" font-family="Arial, sans-serif" font-size="10" fill="#666">• Add neuron</text>

  <text x="715" y="456" font-family="Arial, sans-serif" font-size="10" fill="#666">• Add connection</text>

  <text x="715" y="474" font-family="Arial, sans-serif" font-size="10" fill="#666">• Delete connection</text>

  <text x="715" y="492" font-family="Arial, sans-serif" font-size="10" fill="#666">• Change activation fn</text>



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    Termination Criteria

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  <text x="55" y="485" font-family="Arial, sans-serif" font-size="10" fill="#666">✓ Target fitness reached</text>

  <text x="55" y="503" font-family="Arial, sans-serif" font-size="10" fill="#666">✓ Max generations</text>

  <text x="55" y="521" font-family="Arial, sans-serif" font-size="10" fill="#666">✓ Fitness stagnation</text>



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    Each iteration improves the population. Over many generations, optimal neural network topologies emerge.

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