Kenneth O. Stanley (2006)
Patterns Without Development
University of Central Florida Dept. of EECS Technical Report CS-TR-06-01 (40 pages)
Please note: This technical report is no longer available because it has been superseded by the journal article "Compositional Pattern Producing Networks: A Novel Abstraction of Development " (above)
Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings, i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike most computational abstractions of natural development, CPPNs do not include a developmental phase, differentiating them from developmental encodings. Instead of development, CPPNs employ compositions of functions derived from gradient patterns present in developing natural organisms. In this paper, a variant of the NeuroEvolution of Augmenting Topologies (NEAT), method called CPPN-NEAT, evolves increasingly complex CPPNs. This novel approach promises greater efficiency than developmental encoding and also evolves patterns with strikingly natural characteristics.