Divine AI Creator — Generative Art Evolution Simulator

The Creator AI

A small neural network (shown floating above the gallery) paints each tile from pure math — no photos, no training data. Click a tile to make it the parent of the next generation.

Mutation rate 35%

Network complexity 7 nodes

Generation Stats

Generation

Diversity

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Parent nodes

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How it works

Each tile is painted by a Compositional Pattern-Producing Network (CPPN) — a genuine class of generative neural network used in evolutionary art and NEAT-style indirect encodings. For every pixel it takes coordinates (x, y), radius r = √(x²+y²) and a bias term, and pushes them through a small hidden layer:

h_i = f_i( Σ_j w_in[i][j] · input_j )
R,G,B = σ( Σ_i w_out[c][i] · h_i + b_c )

Each hidden node i gets its own activation function f_i — sine, cosine, tanh, or a Gaussian bump — which is exactly why CPPNs paint swirls, ripples and blobs instead of photographic noise: composing periodic and smooth functions of position naturally produces coherent geometric patterns.

Click a tile — selects it as the parent (yellow border) and updates the neural-graph diagram above the gallery to show its actual weighted connections.
Evolve from Selected — spawns 9 children by copying the parent's weights and perturbing each one with probability = mutation rate, occasionally swapping a node's activation function too. This is genuine weight-space mutation, the same operator used in real neuroevolution (e.g. NEAT).
Network complexity — changes the hidden-layer size, which changes what the network is even capable of expressing, so it starts a fresh generation 0 gallery.
Mirror symmetry — folds the x-coordinate (|x| instead of x) before evaluation, a common CPPN trick for bilaterally symmetric art.
Diversity — mean pixel-by-pixel difference across the 9 tiles; it should fall as a mutation rate near 0% converges the gallery toward clones of the parent, and rise again near 90%.

Selected parent Positive weight Negative weight

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