For the effective calculation of the information density within the connectivity patterns of the substrate, we need to use an appropriate data structure. We need to employ a data structure that allows an effective search through the two-dimensional substrate space at different levels of granularity. In computer science, there is a data structure that perfectly fits these requirements. This structure is the quadtree.
The quadtree is a data structure that allows us to organize an effective search through two-dimensional space by splitting any area of interest into four subareas. Each of these subareas consequently becomes a leaf of a tree, with the root node representing the initial region.
ES-HyperNEAT employs the quadtree data structure to iteratively look for the new connections and nodes in the substrate, starting from...
