Artificial Evolution of Arbitrary Self-Replicating Structures
Zhijian Pan and James A. Reggia
Numerous computational studies over the last half century have sought to create self-replicating structures or “machines”. Cellular automata (CA) have been the most widely used method in these studies, with manual designs yielding a number of specific structures or configurations capable of self-replication. While previous attempts at automated design of self-replicating structures using genetic algorithms have produced some interesting results, these studies were limited by the enormous computational costs incurred. In this paper we describe our recent use of genetic programming (GP) methods to automatically discover CA rule sets that produce self-replication of arbitrary given structures. Our initial results have produced larger, more rapidly replicating structures than past evolutionary models while requiring only a small fraction of the computational time needed in past similar studies. We conclude that genetic programming provides a very powerful tool for discovering novel CA models of self-replicating systems and possibly other complex systems.