variantsHyperNEAT

HyperNEAT is an extension of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm designed to evolve neural networks with a geometrically determined structure. Variants of HyperNEAT explore different ways to represent and generate these structures. One common variant involves using different geometric substrate representations. Instead of a fixed grid, some variants might use a more flexible, adaptive substrate that can change its resolution or connectivity based on the problem's complexity. Another area of variation lies in the way the CPPNs, which generate the network weights and connections, are configured. This can involve altering the activation functions used within the CPPNs or modifying the optimization process for the CPPN parameters. Some variants also focus on improving the scalability of HyperNEAT by developing more efficient methods for substrate generation or CPPN evaluation, especially for very large networks. Furthermore, research has explored incorporating different forms of prior knowledge into the CPPN generation process to guide the evolved network structures towards more relevant solutions for specific tasks. These variants aim to enhance HyperNEAT's effectiveness across a wider range of applications and to address limitations in its original formulation.