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An enhanced hypercube-based encoding for evolving the placement, density, and connectivity of neurons.

Sebastian Risi¹, Kenneth O Stanley

¹University of Central Florida, FL, USA. sebastian.risi@gmail.com

Artificial Life

|September 4, 2012

Summary

Evolvable-substrate HyperNEAT (ES-HyperNEAT) allows artificial neural networks (ANNs) to evolve neuron placement automatically, overcoming limitations of previous neuroevolution methods. This advances the discovery of complex neural structures with natural properties.

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Area of Science:

Artificial intelligence
Computational neuroscience
Evolutionary computation

Background:

Biological brains evolve complex structures through natural selection.
Neuroevolution (NE) algorithms evolve artificial neural networks (ANNs) but struggle to match biological brain capabilities.
HyperNEAT evolved ANNs by linking connectivity patterns to geometry, but neuron placement was user-defined.

Purpose of the Study:

To introduce Evolvable-substrate HyperNEAT (ES-HyperNEAT) for evolving ANNs with dynamic neuron placement.
To overcome the limitation of a priori neuron configuration in HyperNEAT.
To enable ANNs to develop natural properties like neural topography and varying density.

Main Methods:

ES-HyperNEAT deduces node geometry from weight patterns, eliminating the need for explicit placement.
The approach allows for the evolution of neuron locations and regions of varying density.
Demonstrated through multi-task, maze navigation, and modular retina tasks.

Main Results:

ES-HyperNEAT successfully evolved ANNs with emergent properties such as neural topography and geometric regularity.
The method allows for holistic resolution increases during evolution.
Compact indirect encoding can be seeded to bias the evolutionary search towards desired topographies.

Conclusions:

ES-HyperNEAT significantly expands the range of neural structures discoverable through evolution.
This method represents a significant step towards creating ANNs that more closely mimic biological brain evolution.
The ability to evolve neuron placement and density offers greater flexibility and complexity in ANN design.