Artificial Evolution of Arbitrary Self-Replicating Cellular Automata

Since John von Neumann's seminal work on developing cellular automata models of self-replication, there have been numerous computational studies that have sought to create self-replicating structures or "machines". Cellular automata (CA) has been the most widely used method in these studies, with manual designs yielding a number of specific self-replicating structures. However, it has been found to be very difficult, in general, to design local state-transition rules that, when they operate concurrently in each cell of the cellular space, produce a desired global behavior such as self-replication. This has greatly limited the number of different self-replicating structures designed and studied to date. In this dissertation, I explore the feasibility of overcoming this difficulty by using genetic programming (GP) to evolve novel CA self-replication models. I first formulate an approach to representing structures and rules in cellular automata spaces that is amenable to manipulation by the genetic operations used in GP. Then, using this representation, I demonstrate that it is possible to create a "replicator factory" that provides an unprecedented ability to automatically generate a whole class of new self-replicating structures and that allows one to systematically investigate the properties of replicating structures as one varies the initial configuration, its size, shape, symmetry, and allowable states. This approach is then extended to incorporate multi-objective fitness criteria, resulting in production of diversified replicators. For example, this allows generation of target structures whose complexity greatly exceeds that of the seed structure itself. Finally, the extended multi-objective replicator factory is further generalized into a structure/rule co-evolution model, such that replicators with unspecified seed structures can also be concurrently evolved, resulting in different structure/rule combinations and having the capability of not only replicating but also carrying out a secondary pre-specified task with different strategies. I conclude that GP provides a powerful method for creating CA models of self-replication

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

Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata

We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification problem and to the novel generation of 'chaotic' CA. Our work furthermore introduces a new variant of PSO, the Binary Global-Local PSO (BGL-PSO)

A guided tour of asynchronous cellular automata

Research on asynchronous cellular automata has received a great amount of attention these last years and has turned to a thriving field. We survey the recent research that has been carried out on this topic and present a wide state of the art where computing and modelling issues are both represented.Comment: To appear in the Journal of Cellular Automat

Maximizing the Adjacent Possible in Automata Chemistries

Automata chemistries are good vehicles for experimentation in open-ended evolution, but they are by necessity complex systems whose low-level properties require careful design. To aid the process of designing automata chemistries, we develop an abstract model that classifies the features of a chemistry from a physical (bottom up) perspective and from a biological (top down) perspective. There are two levels: things that can evolve, and things that cannot. We equate the evolving level with biology and the non-evolving level with physics. We design our initial organisms in the biology, so they can evolve. We design the physics to facilitate evolvable biologies. This architecture leads to a set of design principles that should be observed when creating an instantiation of the architecture. These principles are Everything Evolves, Everything’s Soft, and Everything Dies. To evaluate these ideas, we present experiments in the recently developed Stringmol automata chemistry. We examine the properties of Stringmol with respect to the principles, and so demonstrate the usefulness of the principles in designing automata chemistries