Genetic cellular neural networks for generating three-dimensional geometry

TL;DR

This paper introduces a novel method combining genetic algorithms with cellular neural networks and mesh growth to procedurally generate complex 3D shapes that mimic biological development.

Contribution

It presents a new approach integrating neural networks, mesh growth, and genetic algorithms for procedural 3D shape generation.

Findings

Enables emergent complex 3D shapes from genetic codes.

Neural networks at mesh vertices facilitate shape growth.

Approach mimics biological development complexity.

Abstract

There are a number of ways to procedurally generate interesting three-dimensional shapes, and a method where a cellular neural network is combined with a mesh growth algorithm is presented here. The aim is to create a shape from a genetic code in such a way that a crude search can find interesting shapes. Identical neural networks are placed at each vertex of a mesh which can communicate with neural networks on neighboring vertices. The output of the neural networks determine how the mesh grows, allowing interesting shapes to be produced emergently, mimicking some of the complexity of biological organism development. Since the neural networks' parameters can be freely mutated, the approach is amenable for use in a genetic algorithm.