Biological organisms are among the most robust systems known to man. Their robustness is based on a set of processes which cannot be adapted directly to the world of silicon, but can provide an inspiration for the design of robust circuits. This paper introduces a multiplexerbased FPGA which we made capable of self-test and self-repair using an approach loosely based on biological mechanisms at the cellular level. The system is designed to provide on-line self-test and selfrepair using a completely distributed system and a minimal amount of additional logic. 1.

Self-reproducing, cellular automata-based systems developed to date broadly fall under two categories; the first consists of machines which are capable of performing elaborate tasks, yet are too complex to simulate, while the second consists of extremely simple machines which can be entirely implemented, yet lack any additional functionality aside from self-reproduction. In this paper we present a self-reproducing system which is completely realizable, while capable of executing any desired program, thereby exhibiting universal computation. Our starting point is a simple self-reproducing loop structure onto which we "attach" an executable program (Turing machine) along with its data. The three parts of our system (loop, program, data) are all reproduced, after which the program is run on the given data. The system reported in this paper has been simulated in its entirety; thus, we attain a viable, self-reproducing machine with programmable capabilities. 1 Introduction The study of art...

Previous computational models of self-replication using cellular automata have been manually designed, a difficult and time-consuming process. We show here how genetic algorithms can be applied to automatically discover rules governing self-replicating structures. The main difficulty in this problem lies in the choice of the fitness evaluation technique. The solution we present is based on a multiobjective fitness function consisting of three independent measures: growth in number of components, relative positioning of components, and the multiplicity of replicants. We introduce a new paradigm for cellular automata models with weak rotational symmetry, called orientation insensitive input, and hypothesize that it facilitates discovery of self-replicating structures by reducing search-space sizes. Experimental yields of self-replicating structures discovered using our technique are shown to be statistically significant. The discovered self-replicating structures compare favorably in terms of simplicity with those generated manually in the past, but differ in unexpected ways. These results suggest that further exploration in the space of possible self-replicating structures will yield additional new structures. Furthermore, this research sheds light on the process of creating self-replicating structures, opening the door to future studies on the discovery of novel self-replicating molecules and self-replicating assemblers in nanotechnology.

This paper gives details of Squirm3, a new artificial environment based on a simple physics and chemistry that supports self-replicating molecules somewhat similar to DNA.The self-replicators emerge spontaneously from a random soup given the right conditions.Interactions between the replicators can result in mutated versions that can out-perform their parents.We show how artificial chemistries such as this one can be implemented as a cellular automaton.We concur with [9] that artificial chemistries are a good medium in which to study early evolution.

We succeeded for the first time in constructing evolutionary systems on a simple 9-state 5-neighbor cellular automata (CA) space by utilizing Langton's self-reproducing loop. CA are deterministic dynamical systems capable of representing extremely complex nonlinear phenomena, where time, space and states of sites are all discrete. Many CA models of self-reproductive behavior of theoretical organisms have so far been energetically studied, but the evolutionary process of organisms driven by variation and natural selection has never been realized on CA space yet. In this dissertation, we added three improvements into Langton's loop, i.e., to realize a kind of death by introducing a new dissolving state `8' into the set of states of the CA, to enhance the adaptability (a degree of the variety of situations in which the structures in the CA space can operate regularly) of the selfreproductive mechanism described by the state-transition rules of the CA, and to modify the initial structure o...

Biological organisms are among the most intricate structures known to man, exhibiting highly complex behavior through the massively parallel cooperation of numerous relatively simple elements, the cells. As the development of computing systems approaches levels of complexity such that their synthesis begins to push the limits of human intelligence, engineers are starting to seek inspiration in nature for the design of computing systems, both at the software and at the hardware levels. This paper will present one such endeavor, notably an attempt to draw inspiration from biology in the design of a novel digital circuit: a field-programmable gate array (FPGA). This reconfigurable logic circuit will be endowed with two features motivated and guided by the behavior of biological systems: self-replication and self-repair. 1

Fault tolerance has always been a standard feature of electronic systems intended for long-term missions. However, the high complexity of modern systems makes the incorporation of fault tolerance a difficult task. Novel approaches to fault tolerance can be achieved by drawing inspiration from nature. Biological organisms possess characteristics such as healing and learning that can be applied to the design of fault-tolerant systems. This paper extends the work on bio-inspired fault-tolerant systems at the University of York. It is proposed that by combining embryonic arrays with an immune inspired network, it is possible to achieve systems with higher reliability. URL: http://www.immunotronics.co.uk AND http://www.bioinspired.com

It has been argued that a central objective of nanotechnology is to make products inexpensively, and that self-replication is an effective approach to very low-cost manufacturing. The research presented here is intended to be a step towards this vision. We describe a computational simulation of nanoscale machines floating in a virtual liquid. The machines can bond together to form strands (chains) that self-replicate and self-assemble into user-specified meshes. There are four types of machines and the sequence of machine types in a strand determines the shape of the mesh they will build. A strand may be in an unfolded state, in which the bonds are straight, or in a folded state, in which the bond angles depend on the types of machines. By choosing the sequence of machine types in a strand, the user can specify a variety of polygonal shapes. A simulation typically begins with an initial unfolded seed strand in a soup of unbonded machines. The seed strand replicates by bonding with free machines in the soup. The child strands fold into the encoded polygonal shape, and then the polygons drift together and bond to form a mesh. We demonstrate that a variety of polygonal meshes can be manufactured in the simulation, by simply changing the sequence of machine types in the seed.

Abstract. We consider temporal aspects of self-replication and evolvability – in particular, the massively asynchronous parallel and distributed nature of living systems. Formal views of self-reproduction and time are surveyed, and a general asynchronization construction for automata networks is presented. Evolution and evolvability are distinguished, and the evolvability characteristics of natural and artificial examples are overviewed. Minimal implemented evolvable systems achieving (1) asynchronous self-replication and evolution, as well as (2) protocultural transmission and evolution, are presented and analyzed for evolvability. Developmental genetic regulatory networks (DGRNs) are suggested as a novel paradigm for massive asynchronous computation and evolvability. An appendix classifies modes of life (with different degrees of aliveness) for natural and artificial living systems and possible transitions between them. 1 Models of Time: Logical vs. Physical Time We consider time in discrete dynamical systems. St. Augustine considered time as something intuitively graspable yet ineffable. Varshavsky distinguished two kinds of time: Time as a logical variable in a system defined by events vs. time as an independent physical variable [96], and studied self-timing and asynchrony theory for computing devices as the problem of reconciling the two types of time via design of system timing for the appropriate functioning asynchronous devices interacting with external environments. For a single observer or location, we can consider three main views of the (logical) time:

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