Automated discovery of self-replicating structures in cellular space automata models

This thesis demonstrates for the first time that it is possible to automatically discover self-replicating structures in cellular space automata models rather than, as has been done in the past, to design them manually. Self-replication is defined as the process an entity undergoes in constructing a copy of itself. Von Neumann was the first to investigate artificial self-replicating structures and did so in the context of cellular automata, a cellular space model consisting of numerous finite-state machines embedded in a regular tessellation. Interest in artificial self-replicating systems has increased in recent years due to potential applications in molecular-scale manufacturing, programming parallel computing systems, and digital hardware design, and also as part of the field of artificial life. In this dissertation, genetic algorithms are used with a cellular automata framework for the first time to automatically discover self-replicating structures. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past but differ in unexpected ways. This dissertation presents representative samples of the self-replicating structures and analyses them both quantitatively and qualitatively. In order to effectively search the underlying rule space of such automata models, a fitness function consisting of three independent criteria is designed and successfully applied. Also, a new cellular space automata model called effector automata is introduced. It is shown to be more computationally feasible and to promote the discovery of more self-replicating structures as compared to the cellular automata models used in previous studies. In addition, a new paradigm for cellular space models with weak rotational symmetry called component-sensitive input is introduced and shown to facilitate discovery of self-replicating structures. The results presented suggest that genetic algorithms can be powerful tools for exploring the space of possible self-replicating structures. Furthermore, this research sheds light on the nature of creating self-replicating structures and opens the door to further studies that could eventually lead to the discovery of new self-replicating molecular structures.