A hybrid architecture for programmable computing and evolutionary learning

Abstract

The relation between the structure and the computational function of the brain must have a plastic quality in order for it to have developed through the Darwinian mechanism of variation and selection. It is likely that such structure-function plasticity plays a role in ontogenetic learning as well. Present-day digital computers, by contrast, are highly programmable. But this powerful property entails rigidly defined structure-function relations that are generally incompatible with self-organization through evolution. Hybrid systems could combine the advantages of both programmability and evolvability. The system described in this paper comprises a conventional machine that communicates with an evolution-amenable cellular automaton system. The latter learns to use its low-level parallelism through a “copy thy neighbor” evolutionary algorithm. We describe some simulation experiments with the system and consider how direct fabrication might be achieved using silicon as well as nonsilicon technologies.