In a traditional cellular automaton (CA) a cell is implemented by a rule table defining its state at the next time step, given its present state and those of its neighbors. The cell thus deals only with states. We present a novel CA where the cell handles data and signals. The cell is designed as a digital system comprising a processing unit and a control unit. This allows the realization of various growing structures, including self-replicating loops and biomorphs. We also describe the hardware implementation of these structures within our electronic wall for bio-inspired applications, the BioWall.

This letter describes an evolutionary system for creating lifelike three-dimensional plants and flowers, our main goal being the facilitation of producing realistic plant imagery. With these two goals in mind—ease of generation and realism—we designed the plant genotype and the genotype-to-phenotype mapping. Diversity in our system comes about through two distinct processes—evolution and randomization—allowing the creation not only of single plants but of entire gardens and forests. Thus, we are able to readily produce natural-looking artificial scenes.

Self-replicating loops presented to date are essentially worlds unto themselves, inaccessible to the observer once the replication process is launched. In this article we present the design of an interactive self-replicating loop of arbitrary size, wherein the user can physically control the loop's replication and induce its destruction. After introducing the BioWall, a reconfigurable electronic wall for bio-inspired applications, we describe the design of our novel loop and delineate its hardware implementation in the wall.

The field of artificial life (Alife) is replete with documented instances of emergence , though debate still persists as to the meaning of this term. We contend that, in the absence of an acceptable definition, researchers in the field would be well served by adopting an emergence certification mark that would garner approval from the Alife community. Toward this end, we propose an emergence test , namely, criteria by which one can justify conferring the emergence label.

The study of artificial self-replicating structures or machines has been taking place now for almost half a century. My goal in this article is to present an overview of research carried out in the domain of self-replication over the past 50 years, starting from von Neumann's work in the late 1940s and continuing to the most recent research efforts. I shall concentrate on computational models, that is, ones that have been studied from a computer science point of view, be it theoretical or experimental. The systems are divided into four major classes, according to the model on which they are based: cellular automata, computer programs, strings (or strands), or an altogether different approach. With the advent of new materials, such as synthetic molecules and nanomachines, it is quite possible that we shall see this somewhat theoretical domain of study producing practical, real-world applications.

In this short article we argue that von Neumann's quintessential message with respect to self-replicating automata is genotype + ribotype = phenotype. Self-replication of his universal constructor occurs in analogy to nature: The description (genotype) written on the input tape is translated via a ribosome (ribotype) so as to create the offspring universal constructor (phenotype).

Some of the major outstanding problems in biology are related to issues of emergence and evolution. These include: (a) how populations of organisms traverse their adaptive landscapes; (b) what the relation between adaptedness and fitness is; and (c) the formation of multicellular organisms from basic units or cells. In this article we study these issues using a model that is both general and simple . The system, derived from the CA (cellular automata) model, consists of a two-dimensional grid of interacting organisms that may evolve over time. We first present designed multicellular organisms that display several interesting behaviors, including reproduction, growth, and mobility. We then turn our attention to evolution in various environments , including an environment in which competition for space occurs, an IPD (Iterated Prisoner's Dilemma) environment, an environment of spatial niches, and an environment of temporal niches. One of the advantages of artificial life (AL) models is the opportunities they offer in performing in-depth studies of the evolutionary process. This is accomplished in our case by observing not only phenotypic effects but also such measures as fitness, operability, energy and the genescape. Our work sheds light on the problems raised above, and offers a possible path toward the long-term, two-fold goal of ALife research: (a) increasing our understanding of biology, and (b) enhancing our understanding of artificial models, thereby providing us with the ability to improve their performance.