Structure Of Cellular Automata Research Articles

GAN-Based LUCC Prediction via the Combination of Prior City Planning Information and Land-Use Probability

Currently, the world is in a period of urbanization that will accelerate the processes of land-use cover and ecological change. Thus, establishing a land-use and land-cover change (LUCC) prediction and simulation model is of great significance for understanding the process of urban change and assessing its ecological impact. In previous studies, LUCC prediction models have been mainly based on cellular automata structures that calculate a future state pixel by pixel through transition rules. Because these transition rules are usually based on the global state and each pixel is calculated according to these fixed rules, the results of these methods have room for improvement in terms of generating details and heterogeneity. In this article, a generative adversarial network (GAN)-based LUCC prediction model using multiscale local spatial information is proposed. The model is based on a pix2pix GAN and an attention structure that predicts future land use through multiscale local spatial information. To validate our model, Shenzhen, a region that is experiencing rapid urbanization, was chosen as the source of the experimental data. The results indicate that the proposed method achieved the highest accuracy in both short-time interval and long-time interval scenarios. In addition, the results of the proposed method were also closest to the ground truth from the perspective of the landscape pattern.

An Improved Atom Search Optimization With Cellular Automata, a Lévy Flight and an Adaptive Weight Strategy

Atom search optimization (ASO) is a newly developed metaheuristic algorithm inspired by molecular basis dynamics. The paramount challenge in ASO is that it is prone to stagnation in local optima and premature convergence. To solve these issues, this paper uses a cellular automata structure, a Lévy flight and adaptive weight strategies to help ASO balance exploration and exploitation; the proposed method is named CALFASO. First, the cellular automata structure provides more information exchange for the population and enriches the diversity of the population. Second, a Lévy flight has the characteristics of a random walk, its short-step long-term search can improve the global search capability of the algorithm, and its short-term long-step mutations can help the algorithm jump out of local optima. Third, adaptive weights can effectively improve convergence performance. Finally, the proposed CALFASO was evaluated on the CEC2017 test set and real-world engineering problems and compared with classical and novel evolutionary algorithms to verify its performance. The experimental results and statistical analysis show that the performance of the proposed CALFASO algorithm is better than that of the other selected algorithms.

Cellular Automata Based Test Design for Coherence Verification in 3D Caches

To provide high vertical interconnection density between device tiers, Through Silicon Via (TSV) offers a promising solution in 3D caches. It reduces the length of global interconnection and ensures high speed cache memory access. Maintaining coherency of shared data in such caches is, however, very crucial and, therefore, demands that the reliability and accuracy of TSVs as well as the cache coherence controller (CC) are to be ensured. In the current work, we propose an elegant test solution for at-speed detection of stuck-at-faults in TSVs (offline test) as well as verification for the functioning of CC (online test). The proposed test structure is designed around the modular and cascadable structure of Cellular Automata (CA) to achieve a cost-effective realization of test and coherence verification in 3D caches with high degree of scalability. It ensures correct decisions in more than 71% cases even if the test hardware is subjected to single stuck-at-fault.

Structure and Reversibility of 2D von Neumann Cellular Automata Over Triangular Lattice

Even though the fundamental main structure of cellular automata (CA) is a discrete special model, the global behaviors at many iterative times and on big scales could be a close, nearly a continuous, model system. CA theory is a very rich and useful phenomena of dynamical model that focuses on the local information being relayed to the neighboring cells to produce CA global behaviors. The mathematical points of the basic model imply the computable values of the mathematical structure of CA. After modeling the CA structure, an important problem is to be able to move forwards and backwards on CA to understand their behaviors in more elegant ways. A possible case is when CA is to be a reversible one. In this paper, we investigate the structure and the reversibility of two-dimensional (2D) finite, linear, triangular von Neumann CA with null boundary case. It is considered on ternary field [Formula: see text] (i.e. 3-state). We obtain their transition rule matrices for each special case. For given special triangular information (transition) rule matrices, we prove which triangular linear 2D von Neumann CAs are reversible or not. It is known that the reversibility cases of 2D CA are generally a much challenged problem. In the present study, the reversibility problem of 2D triangular, linear von Neumann CA with null boundary is resolved completely over ternary field. As far as we know, there is no structure and reversibility study of von Neumann 2D linear CA on triangular lattice in the literature. Due to the main CA structures being sufficiently simple to investigate in mathematical ways, and also very complex to obtain in chaotic systems, it is believed that the present construction can be applied to many areas related to these CA using any other transition rules.

Plasmonic Nanostructured Cellular Automata

In this work, we have investigated the scattering plasmonic resonance characteristics of silver nanospheres with a geometrical distribution that is modelled by Cellular Automata using time-domain numerical analysis. Cellular Automata are discrete mathematical structures that model different natural phenomena. Two binary one-dimensional Cellular Automata rules are considered to model the nanostructure, namely rule 30 and rule 33. The analysis produces three-dimensional scattering profiles of the entire plasmonic nanostructure. For the Cellular Automaton rule 33, the introduction of more Cellular Automata generations resulted only in slight red and blue shifts in the plasmonic modes with respect to the first generation. On the other hand, while rule 30 introduced significant red shifts in the resonance peaks at early generations, at later generations however, a peculiar effect is witnessed in the scattering profile as new peaks emerge as a feature of the overall Cellular Automata structure rather than the sum of the smaller parts that compose it. We strongly believe that these features that emerge as a result adopting the different 256 Cellular Automata rules as configuration models of nanostructures in different applications and systems might possess a great potential in enhancing their capability, sensitivity, efficiency, and power utilization.

Cellular automata model of an artificial biological cell in the form of a self-replicating structure

Two cellular automata models of an artificial biological cell, which can be elements of computing devices that mimic the properties of living organisms (growth, self-reproduction, and self-repair), are presented. The models are based on the parallel substitution algorithm, which is a spatial model for representing fine-grained parallel algorithms and architectures. An artificial biological cell is constructed from a genome applied to the input tape of the cellular automata structure. The result is a model of an artificial biological cell, which contains a phenotype as a set of fixed data and a genotype as a set of mobile data.

Spatiotemporal order, disorder, and propagating defects in homogeneous system of relaxation oscillators

A variety of complex spatial patterns relevant to chemical and biological systems can be generated through reaction-diffusion mechanisms. In this paper, we show that diffusive coupling through the inactivating component in a system of relaxation oscillators extends such complexity to the temporal domain, generating remarkable spatiotemporal phenomena. We provide analytic explanations of the antiphase synchronization and spatially patterned oscillatory death regimes. We report a chimera state where patches with distinct dynamics coexist and also observe propagating phase defects resembling persistent structures in cellular automata that may be used for computation.

Cellular-based Population to Enhance Genetic Algorithm for Assignment Problems

In this paper, we describe a new mechanism of cellular selection as an improved Genetic Algorithm for some optimization problems like Cellular Channel assignment, which have multi feasible/optimum solution per one case. Con- sidering the problems and the nature of relationship among individuals in population, we use 2-dimension Cellular Autom- ata in order to place the individuals onto its cells to make the locality and neighborhood on the Hamming distance basis. This idea as 2D Cellular Automata Hamming GA has introduced locality in Genetic Algorithms and global knowledge for their selection process on Cells of 2D Cellular Automata. The selection based on 2D Cellular Automata can ensure main- taining population diversity and fast convergence in the genetic search. The cellular selection of individuals is controlled based on the structure of cellular automata, to prevent the fast population diversity loss and improve the convergence per- formance during the genetic search.

Computational Discovery of Instructionless Self-Replicating Structures in Cellular Automata

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

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.

A deterministic built-in self-test generator based on cellular automata structures

This paper proposes a new approach for designing a cost-effective, on-chip, deterministic, built-in, self-test generator. Given a set of precomputed test vectors (obtained by an ATPG tool) with a predetermined fault coverage, a simple test vector generator (TVG) is synthesized to apply the given test set in a minimal test time. To achieve this objective, cellular automata (CA) structures have been used in which the rule space is not limited to the linear rules commonly used in CA studies recently. Based on some new notations and new formulations of CA properties, two techniques are developed to synthesize such a TVG which is used to generate an ordered/unordered deterministic test vector set. The resulting TVG is very efficient in terms of hardware size and speed performance, and is very regular and testable. Simulation of various benchmark combinational circuits has given good results when compared to alternative solutions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>