This article illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex artificial neural networks (ANNs). The artificial developmental system can develop a graph grammar into a modular ANN made of a combination of simpler subnetworks. A genetic algorithm is used to evolve coded grammars that generate ANNs for controlling six-legged robot locomotion. A mechanism for the automatic definition of neural subnetworks is incorporated Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher-level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of subnetworks is suppressed. We support our argument with pictures that describe the steps of development, how ANN structures are evolved, and how the ANNs compute.

A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.

No abstract

No abstract