C++ ES-HyperNEAT algorithm implementation
The HyperNEAT algorithm uses evolution to train neural networks. Genome neural networks, known as Compositional Pattern-Producing Networks (CPPN), are randomly generated via mutations and crossover. This is then used to "paint" a pattern on a 4-dimensional hypercube, which represents the weights and biases of the resulting phenome neural network.
The phenome neural network is what is actually evaluated when running a simulation.
Through the process of Darwinian natural selection, the genomes will eventually converge towards creating a network that can maximize the fitness function and solve the task.
To build the demo executables
- Create a build folder and go to it
mkdir build && cd build - Run
cmake .. && make -j 3
Code and documentation Copyright (c) 2022 Keith Leonardo
Code released under the MIT License.