Computational Discovery of Instructionless Self-Replicating Structures in Cellular Automata

2010 ◽  
Vol 16 (1) ◽  
pp. 39-63 ◽  
Author(s):  
Zhijian Pan ◽  
James A. Reggia
Keyword(s):  
Information Processing ◽  
Cellular Automata ◽  
Genetic Programming ◽  
Secondary Structures ◽  
Computational Discovery ◽  
Processing Properties ◽  
The Given ◽  
Self Replication ◽  
Instruction Sequence

Cellular automata models have historically been a major approach to studying the information-processing properties of self-replication. Here we explore the feasibility of adopting genetic programming so that, when it is given a fairly arbitrary initial cellular automata configuration, it will automatically generate a set of rules that make the given configuration replicate. We found that this approach works surprisingly effectively for structures as large as 50 components or more. The replication mechanisms discovered by genetic programming work quite differently than those of many past manually designed replicators: There is no identifiable instruction sequence or construction arm, the replicating structures generally translate and rotate as they reproduce, and they divide via a fissionlike process that involves highly parallel operations. This makes replication very fast, and one cannot identify which descendant is the parent and which is the child. The ability to automatically generate self-replicating structures in this fashion allowed us to examine the resulting replicators as their properties were systematically varied. Further, it proved possible to produce replicators that simultaneously deposited secondary structures while replicating, as in some past manually designed models. We conclude that genetic programming is a powerful tool for studying self-replication that might also be profitably used in contexts other than cellular spaces.

PROPERTIES OF SELF-REPLICATING CELLULAR AUTOMATA SYSTEMS DISCOVERED USING GENETIC PROGRAMMING

2007 ◽  
Vol 10 (supp01) ◽  
pp. 61-84 ◽  
Author(s):  
ZHIJIAN PAN ◽  
JAMES REGGIA ◽  
DONGHONG GAO
Keyword(s):  
Cellular Automata ◽  
Genetic Programming ◽  
A Priori ◽  
Computation Time ◽  
Initial Configuration ◽  
Number Of Components ◽  
Arbitrary Structure ◽  
Wide Range ◽  
State Changes ◽  
Self Replication

We recently formulated an approach to representing structures in cellular automata (CA) spaces, and the rules that govern cell state changes, that is amenable to manipulation by genetic programming (GP). Using this approach, it is possible to efficiently generate self-replicating configurations for fairly arbitrary initial structures. Here, we investigate the properties of self-replicating systems produced using GP in this fashion as the initial configuration's size, shape, symmetry, allowable states, and other factors are systematically varied. We find that the number of GP generations, computation time, and number of resulting rules required by an arbitrary structure to self-replicate are positively and jointly correlated with the number of components, configuration shape, and allowable states in the initial configuration, but inversely correlated with the presence of repeated components, repeated sub-structures, and/or symmetric sub-structures. We conclude that GP can be used as a "replicator factory" to produce a wide range of self-replicating CA configurations, and that the properties of the resulting replicators can be predicted in part a priori. The rules controlling self-replication that are created by GP generally differ from those created manually in past CA studies.

Simulating Self-Regeneration and Self-Replication Processes Using Movable Cellular Automata with a Mutual Equilibrium Neighborhood

2020 ◽  
Vol 29 (4) ◽  
pp. 741-757
Author(s):  
Kateryna Hazdiuk ◽  
◽  
Volodymyr Zhikharevich ◽  
Serhiy Ostapov ◽  
◽  
...  
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Three Dimensional ◽  
Computer Models ◽  
The Self ◽  
Model Construction ◽  
Natural Phenomena ◽  
Different Types ◽  
Movable Cellular Automata ◽  
Self Replication

This paper deals with the issue of model construction of the self-regeneration and self-replication processes using movable cellular automata (MCAs). The rules of cellular automaton (CA) interactions are found according to the concept of equilibrium neighborhood. The method is implemented by establishing these rules between different types of cellular automata (CAs). Several models for two- and three-dimensional cases are described, which depict both stable and unstable structures. As a result, computer models imitating such natural phenomena as self-replication and self-regeneration are obtained and graphically presented.

Visual control of orientation behaviour in the fly: Part I. A quantitative analysis

1976 ◽  
Vol 9 (3) ◽  
pp. 311-375 ◽  
Author(s):  
Werner Reichardt ◽  
Tomaso Poggio
Keyword(s):  
Information Processing ◽  
Quantitative Analysis ◽  
Visual System ◽  
Sensory Information ◽  
Model Systems ◽  
Suitable Model ◽  
Logical Organization ◽  
Integrative Level ◽  
Processing Properties ◽  
Different Levels

An understanding of sensory information processing in the nervous system will probably require investigations with a variety of ‘model’ systems at different levels of complexity.Our choice of a suitable model system was constrained by two conflicting requirements: on one hand the information processing properties of the system should be rather complex, on the other hand the system should be amenable to a quantitative analysis. In this sense the fly represents a compromise.In these two papers we explore how optical information is processed by the fly's visual system. Our objective is to unravel the logical organization of the fly's visual system and its underlying functional and computational principles. Our approach is at a highly integrative level. There are different levels of analysing and ‘understanding’ complex systems, like a brain or a sophisticated computer.

scholarly journals BLOCK CIPHERS ON THE BASIS OF REVERSIBLE CELLULAR AUTOMATA

2020 ◽  
Vol 10 (1) ◽  
pp. 8-11
Author(s):  
Yuliya Tanasyuk ◽  
Petro Burdeinyi
Keyword(s):  
Cellular Automata ◽  
Block Cipher ◽  
Key Generation ◽  
One Dimensional ◽  
C Programming Language ◽  
C Programming ◽  
Shared Secret ◽  
Reversible Cellular Automata ◽  
The Given ◽  
First Time

The given paper is devoted to the software development of block cipher based on reversible one-dimensional cellular automata and the study of its statistical properties. The software implementation of the proposed encryption algorithm is performed in C# programming language in Visual Studio 2017. The paper presents specially designed approach for key generation. To ensure desired cryptographic stability, the shared secret parameters can be adjusted to contain information needed for creating substitution tables, defining reversible rules, and hiding final data. For the first time, it is suggested to create substitution tables based on iterations of a cellular automaton that is initialized by the key data.

scholarly journals Supportive or information-processing functions of the mature protoplasmic astrocyte in the mammalian CNS? A critical appraisal

2007 ◽  
Vol 3 (3) ◽  
pp. 181-189 ◽  
Author(s):  
Harold K. Kimelberg
Keyword(s):  
Information Processing ◽  
Blood Vessel ◽  
Critical Appraisal ◽  
Vessel Diameter ◽  
Synaptic Activity ◽  
Metabolic Substrates ◽  
Processing Properties ◽  
New Evidence ◽  
The Brain ◽  
Domain Concept

AbstractIt has been proposed that astrocytes should no longer be viewed purely as support cells for neurons, such as providing a constant environment and metabolic substrates, but that they should also be viewed as being involved in affecting synaptic activity in an active way and, therefore, an integral part of the information-processing properties of the brain. This essay discusses the possible differences between a support and an instructive role, and concludes that any distinction has to be blurred. In view of this, and a brief overview of the nature of the data, the new evidence seems insufficient to conclude that the physiological roles of mature astrocytes go beyond a general support role. I propose a model of mature protoplasmic astrocyte function that is drawn from the most recent data on their structure, the domain concept and their syncytial characteristics, of an independent rather than integrative functioning of the ends of each process where the activities that affect synaptic activity and blood vessel diameter will be concentrated.

Genetic Programming

2011 ◽  
pp. 926-931 ◽  
Author(s):  
William H. Hsu
Keyword(s):  
Genetic Programming ◽  
Programming Languages ◽  
Genetic Material ◽  
Biological Evolution ◽  
Validation Data ◽  
Functional Programming Languages ◽  
Real World Problem ◽  
Problem Solving Environment ◽  
Fitness Criterion ◽  
The Given

Genetic programming (GP) is a sub-area of evolutionary computation first explored by John Koza (1992) and independently developed by Nichael Lynn Cramer (1985). It is a method for producing computer programs through adaptation according to a user-defined fitness criterion, or objective function. Like genetic algorithms, GP uses a representation related to some computational model, but in GP, fitness is tied to task performance by specific program semantics. Instead of strings or permutations, genetic programs are most commonly represented as variable-sized expression trees in imperative or functional programming languages, as grammars (O’Neill & Ryan, 2001), or as circuits (Koza et al., 1999). GP uses patterns from biological evolution to evolve programs: • Crossover: Exchange of genetic material such as program subtrees or grammatical rules • Selection: The application of the fitness criterion to choose which individuals from a population will go on to reproduce • Replication: The propagation of individuals from one generation to the next • Mutation: The structural modification of individuals To work effectively, GP requires an appropriate set of program operators, variables, and constants. Fitness in GP is typically evaluated over fitness cases. In data mining, this usually means training and validation data, but cases can also be generated dynamically using a simulator or directly sampled from a real-world problem solving environment. GP uses evaluation over these cases to measure performance over the required task, according to the given fitness criterion.

Biological Traits in Artificial Self-Reproducing Systems

2012 ◽  
Vol 2 (2) ◽  
pp. 69-83
Author(s):  
Eleonora Bilotta ◽  
Pietro Pantano
Keyword(s):  
Cellular Automata ◽  
Cell Development ◽  
Living Systems ◽  
Biological Traits ◽  
Biological Cell ◽  
Universal Models ◽  
Essential Properties ◽  
Self Replication ◽  
The Way

This article presents an artificial taxonomy of 2-D, self-replicating cellular automata (CA) that can be considered as proto-organisms for structure replication. The authors found that the process of self-reproduction is a widespread mechanism. In fact, self-reproducers in 2-D CA are very common and the authors discovered almost 10 methods of self-replication. The structures these systems produce, from ordered to complex ones, are very similar to those found in biological endeavor. After examining self-replicating structures and the way they reproduce, the authors consider their behavior in relation to the patterns they realize and to the function they manifest in realizing artificial organisms. According to the authors, many methods produced by CA are based on universal models of biological cell development. The relevance of such work consists in the goal of modeling the evolution of living systems that can lead us to a better understanding of the essential properties of life.

Self-Description for Construction and Computation on Graph-Rewriting Automata

2007 ◽  
Vol 13 (4) ◽  
pp. 383-396 ◽  
Author(s):  
Kohji Tomita ◽  
Satoshi Murata ◽  
Haruhisa Kurokawa
Keyword(s):  
Structural Change ◽  
Cellular Automata ◽  
State Transition ◽  
Graph Structure ◽  
Graph Rewriting ◽  
Rewriting Systems ◽  
Lattice Space ◽  
Rule Sets ◽  
Graph Rewriting Systems ◽  
Self Replication

This article shows how self-description can be realized for construction and computation in a single framework of a variant of graph-rewriting systems called graph-rewriting automata. Graph-rewriting automata define symbol dynamics on graphs, in contrast to cellular automata on lattice space. Structural change is possible along with state transition. Self-replication based on a self-description is shown as an example of self-description for construction. This process is performed using a construction arm, which is realized as a subgraph, that executes a program described in the graph structure. In addition, a metanode structure is introduced to embed rule sets in the graph structure as self-description for computation. These are regarded as universal graph-rewriting automata that can serve as a model of systems that maintain themselves through replication and modification.

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