Genetic programming: building nanobrains with genetically programmed neural network modules

The author extends ideas concerning the programming methodology called genetic programming, which is the application of the genetic algorithm to the evolution of the signs and weights of fully (self-) connected neural network modules which perform some time-(in)dependent function (e.g. walking, oscillating, etc.) in an optimal manner. Genetically programmed neural net (GenNet) modules are of two types, functional and control. A series of functional GenNets can be evolved and their weights frozen. Control GenNets are then evolved whose outputs are the inputs of the functional GenNets. The author illustrates the conceptual simplicity and the power of genetic programming by showing how a GenNet which teaches a pair of stick legs to walk can be evolved. The author discusses the next major phase of genetic programming research, namely the building of artificial nervous systems (brain building), as well as the tools which will be needed to evolve them, called Darwin machines