To represent hyper-points that are encoding connection weights within the hypercube, the ES-HyperNEAT algorithm employs a quadtree. A quadtree is a tree data structure in which each internal node has exactly four children nodes. This data structure was selected due to its inherent properties, allowing it to represent two-dimensional areas at different levels of granularity. With a quadtree, it is possible to organize an effective search through the two-dimensional space by splitting any area of interest into four subareas, and each of them becomes a leaf of the tree, with root (parent) node representing the original (decomposed) region.
Using the quadtree-based information extraction method, ...
