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.

Some of the major outstanding problems in biology are related to issues of emergence and evolution. These include: (1) how do populations of organisms traverse their adaptive landscapes? (2) what is the relation between adaptedness and fitness? (3) the formation of multi-cellular organisms from basic units or cells. In this paper we study these issues using a model which is both general and simple. The system, derived from the CA (cellular automata) model, consists of a two-dimensional grid of interacting organisms which may evolve over time. We first present designed multi-cellular organisms which display several interesting behaviors including: reproduction, growth, 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 AL models is the opportunities they...

Previous computational models of self-replication in cellular spaces have been manually designed, a very difficult and time-consuming process. This paper introduces the use of genetic algorithms to discover automata rules that govern emergent self-replicating processes. Given dynamically evolving automata, identification of effective fitness functions for self-replicating structures is a difficult task, and we give one solution to this problem. A model consisting of movable automata embedded in a cellular space is introduced and discussed in this context. A genetic algorithm using two fitness criteria was applied to automate rule discovery. After parameter tuning, 6 selfreplicating structures consisting of 2, 3 and 4 automata were discovered over a course of 75 genetic algorithm runs. These results indicate that the fitness functions employed are effective and that genetic algorithms can be used to successfully discover rules for self-replicating structures. 1 Introduction Studying co...

A clear terminology is essential in every research discipline. In the context of ESOA, a lot of confusion exists about the meaning of the terms emergence and self-organisation. One of the sources of the confusion comes from the fact that a combination of both phenomena often occurs in dynamical systems. In this paper a historic overview of the use of each concept as well as a working definition, that is compatible with the historic and current meaning of the concepts, is given. Each definition is explained by supporting it with important characteristics found in the literature. We show that emergence and self-organisation each emphasise different properties of a system. Both phenomena can exist in isolation. The paper also outlines some examples of such systems and considers the combination of emergence and self-organisation as a promising approach in complex multi-agent systems.

Building on the work of Von Neumann, Langton, and Sayama among others, we introduce the rst examples of evolution in populations of self-reproducing con gurations in asynchronous cellular automata. Reliance on a global synchronous update signal has been a limitation of all solutions since the problem of achieving self-production in cellular automata was rst attacked by Von Neumann half a century ago. Results of the author obviate the need for this restriction.

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...

Introduction Every linear cellular automaton ae can be associated with a regular language L(ae) of finite words: L(ae) is the collection of all finite subwords of configurations that arise after one application of the global map of the cellular automaton. Discussions of the language theoretic aspects of linear cellular automata and sofic systems, in particular with respect to their relation to the topology of the space of configurations, can be found in [8], [10] and [7]. In this paper, we will study two measures of complexity associated with L(ae) that are based on minimal finite state machines of a certain type. The first is simply the size of the minimal automaton for L(ae), or, equivalently, the number of left quotients of this language. For the second measure, one can exploit the fact that the languages L(ae) are no

Aesthetic selection and artificial evolution have been two of the more successful companions introduced to the toolbox of electronic image-makers in recent years. This paper examines the niche in which this technique, often associated with the simulation of biological processes, has positioned itself and some of the reasons for its success. Some remarks concerning the meaningfulness of a user's search for images through a genetic space are made and the relationship of this search to traditional artistic practice is examined. Suggestions on how to link other Artificial Life techniques, especially those involving self-organizing and self-assembling systems, with aesthetic evolution and electronic art are also made.

Pattee's semantic closure principle is used to study the characteristics and requirements of evolving material symbols systems. By contrasting agents that reproduce via genetic variation with agents that reproduce via self-inspection, we reach the conclusion that symbols are necessary to attain open-ended evolution, but only if the phenotypes of agents are the result of a material, selforganization process. This way, a study of the inter-dependencies of symbol and matter is presented. This study is based first on a theoretical treatment of symbolic representations, and secondly on simulations of simple agents with matter-symbol inter-dependencies. The agent-based simulations use evolutionary algorithms with indirectly encoded phenotypes. The indirect encoding is based on Fuzzy Development programs, which are procedures for combining fuzzy sets in such a way as to model self-organizing development processes.

Digital organisms have been synthesized based on a computer metaphor of organic life in which CPU time is the “energy ” resource and memory is the “material ” resource. Memory is organized into informational “genetic ” patterns that exploit CPU time for self-replication. Mutation generates new forms, and evolution proceeds by natural selection as different “genotypes ” compete for CPU time and memory space. In addition, new genotypes appear which exploit other “creatures ” for informational or energetic resources. The digital organisms are self-replicating computer programs, however, they can not escape because they run exclusively on a virtual computer in its unique machine language. From a single ancestral “creature ” there have evolved tens of thousands of self-replicating genotypes of hundreds of genome size classes. Parasites evolved, then creatures that were immune to parasites, and then parasites that could circumvent the immunity. Hyper-parasites evolved which subvert parasites to their own reproduction and drive them to extinction. The resulting genetically uniform communities evolve