Evolution Of Automata Research Articles

Research on the Path of Teaching Reform of Colleges and Universities Courses in the Age of Artificial Intelligence

Abstract This paper proposes a simulation of teaching behavior based on metacellular automata in response to the problems in the teaching reform of college courses on the digital platform. Using the rule grid and binary method, the tuple units in the structure of tuple automata are described and represented, followed by the standard combination of many tuples to form a tuple space. Considering that the definition of neighbors is relatively complex, von Neumann-type, Moore-type, and extended Moore-type represent the neighbors of a two-dimensional tuple automaton, and rules for the evolution of tuple automata are also formulated. Led by the constructivist teaching model, the contextual teaching method is divided into five links, on which the contextual teaching model based on metacellular automata is constructed. Simulation and Analysis are used to analyze the teaching reform of college courses in the era of artificial intelligence, for example. The results show that when the simulation condition is the initial satisfaction rate of the system ξ = 0.425, and the teaching strategy n=2, the teaching quality improves with the increase of the response probability, which is in line with the law of teaching, i.e.The teacher should motivate the enthusiasm of learning, stimulate the interest of learning, and entirely play the role of students in teaching. This study aims to promote the reform of college course teaching in the era of artificial intelligence, which will contribute to the improvement of college teaching quality.

Dynamics in Systems with Modulated Symmetries.

We extend the notions of multipole and subsystem symmetries to more general spatially modulated symmetries. We uncover two instances with exponential and (quasi)periodic modulations and provide simple microscopic models in one, two, and three dimensions. Seeking to understand their effect on the long-time dynamics, we numerically study a stochastic cellular automaton evolution that obeys such symmetries. We prove that, in one dimension, the periodically modulated symmetries lead to a diffusive scaling of correlations modulated by a finite microscopic momentum. In higher dimensions, these symmetries take the form of lines and surfaces of conserved momenta. These give rise to exotic forms of subdiffusive behavior with a rich spatial structure influenced by lattice-scale features. Exponential modulation, on the other hand, can lead to correlations that are infinitely long-lived at the boundary while decaying exponentially in the bulk.

Relating SARS-CoV-2 variants using cellular automata imaging

We classify the main variants of the SARS-CoV-2 virus representing a given biological sequence coded as a symbolic digital sequence and by its evolution by a cellular automata with a properly chosen rule. The spike protein, common to all variants of the SARS-CoV-2 virus, is then by the picture of the cellular automaton evolution yielding a visible representation of important features of the protein. We use information theory Hamming distance between different stages of the evolution of the cellular automaton for seven variants relative to the original Wuhan/China virus. We show that our approach allows to classify and group variants with common ancestors and same mutations. Although being a simpler method, it can be used as an alternative for building phylogenetic trees.

Autómata Evolutivo (AE) para el mercado accionario usando Martingalas y un Algoritmo Genético

El objetivo de este trabajo es desarrollar un Autómata Evolutivo (AE) que opera con base a un modelo de martingalas con el que se definen estrategias de inversión, las cuales utilizan información inmediata histórica, límites de ganancia, pérdida y tiempos de permanencia; brinda señales de compra, venta o mantener la posición del activo basadas en la combinación óptima de medias móviles seleccionadas mediante un algoritmo genético. Se probó para dos índices accionarios antes, durante y después de la crisis subprime, mostrando que, cuando los mercados estaban en fase alcista, la estrategia comprar-manter (BH) generó un rendimiento superior al AE; en contraste, para el periodo de crisis observado, el AE logró un rendimiento mayor; finalmente, en todo el periodo de prueba, el rendimiento del AE fue superior. El AE tiene la restricción que solo puede ser usado por una acción/índice por periodo, aunque esto puede ser solventado al ciclarlo por el número de instrumentos del portafolio. La autenticidad del trabajo radica en la combinación de modelos que se complementan para generar un sistema que ayuda en la toma de decisiones.

SPATIAL SYNCHRONIZATION OF CELLULAR AUTOMATA IN EVOLUTIONARY PROCESSES SIMULATION TASKS

Manyappliedtasksaresimulatedbydifferenceequationsthatdescribethevectorofsystemstatesevolutionintime. Howeveritisrequiredtotakeintoaccount the spatial structure of simulated processes or systems in some tasks. Inpaperthepossibilityofaspatio-temporalprocessessimulation by cellular automata is considered. The brief review of two-dimensional cellular automataproperties is provided.Theprincipleofthemostfamoustwo-dimensionalcellularautomata“Game of Life”is described.Alsothegeneralwaytosettheseautomatainananalyticalformby Reaction-Diffusion equation is considered. Concrete forms of the Reaction equation and Diffusion equation are constructed and invariant sets for this system are defined. Thegeneralizationofanalyticalcellularautomata representation in total is provided.Asanexample,themodelofpopulationdevelopmentisconsidered. ItutilizestheclassicFerhulstequation, inwhichthespatialstructureistakenintoaccounthavingformofthecumulativeneighbors’impactonpopulationchangesrate.As per using of analytical form of cellular automata, differentschemasofsystemspatio-temporalcharacteristicscontrol aresuggested. Theseschemasarebasedonfeedback: delayedfeedback(thatis one that uses previous system states)and predictive feedback (that is one that uses predicted system states). As aresultthereismanaged to synchronize spatial configuration of cellular automataand it can be interpreted as stable population development. Particularly, cellular automata could work in cycle with cycle length set earlier. For cellular automata evolution visualization the algorithms and their computer implementation aredeveloped.Discrepancy functionis suggested, due to which it is possible to evaluate the synchronization accuracy. Research results and examples of received configurations are presented.

Ultracold Lattice Gas Automata for Single Trapped Ion Interacting with a Laser Field

AbstractIn the same context of lattice gauge theory, a notion of cellular automaton (CA) is considered as a description of the underlying dynamics of fields evaluated on a discrete lattice. The explorative approach of this work is initiated by taking the automata evolution as an elementary description of fields dynamics on one side and pivotal role of locality on other side to connect the notion of states and transformations to density operators in quantum electrodynamics. In order to parameterize the definition of proposed model in the context of lattice field theory the nonlinear interaction is introduced with neighboring cells. In this paper, analytical and explicit expression of atomic density matrix for single trapped ion interacting with laser field based on cellular automata dynamics are discussed. The definition is further explored by introducing some probabilistic theories as the theoretical framework then it is concluded that the graph of cellular automata in continuum time limit of the CA dynamics leads to Maxwell–Bloch equation.

Delay propagation cellular automata model based on max-plus algebra for robustness evaluations of non-periodic train operation plans

Based on discrete-event dynamic system theory, train operation events in high-speed railway transportation systems are regarded as the basic elements of these dynamic systems. For non-periodic timetable railways in China, based on a max-plus algebra method, a delay propagation cellular automata model is proposed to evaluate the robustness of high-speed train operation plans. The cellular automata evolution rules that can reproduce the delay propagation state of trains mainly consider train safety headway constraints, passenger transfer constraints, and electric multiple unit (EMU) connection constraints. A simulation analysis of actual cases is performed. The simulation results show that the model can be used to evaluate the robustness of the train operation plan. The numerical results show that in the preparation of train operation plans, the proposed model can predict which trains have significant influences on delays in advance and improve the possibility of reducing the occurrence of delays to maintain high-quality service.

Inferring the Limit Behavior of Some Elementary Cellular Automata

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set.

Stability of Cellular Automata Trajectories Revisited: Branching Walks and Lyapunov Profiles

We study non-equilibrium defect accumulation dynamics on a cellular automaton trajectory: a branching walk process in which a defect creates a successor on any neighborhood site whose update it affects. On an infinite lattice, defects accumulate at different exponential rates in different directions, giving rise to the Lyapunov profile. This profile quantifies instability of a cellular automaton evolution and is connected to the theory of large deviations. We rigorously and empirically study Lyapunov profiles generated from random initial states. We also introduce explicit and computationally feasible variational methods to compute the Lyapunov profiles for periodic configurations, thus developing an analog of Floquet theory for cellular automata.

An adaptive semi-supervised Fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images

According to the World Health Organization, breast cancer is the most common cancer in women worldwide, becoming one of the most fatal types of cancer. Mammography image analysis is still the most effective imaging technology for breast cancer diagnosis, which is based on texture and shape analysis of mammary lesions. The GrowCut algorithm is a general-purpose segmentation method based on cellular automata, able to perform relatively accurate segmentation through the adequate selection of internal and external seed points. In this work we propose an adaptive semi-supervised version of the GrowCut algorithm, based on the modification of the automaton evolution rule by adding a Gaussian fuzzy membership function in order to model non-defined borders. In our proposal, manual selection of seed points of the suspicious lesion is changed by a semiautomatic stage, where just the internal points are selected by using a differential evolution algorithm. We evaluated our proposal using 57 lesion images obtained from MiniMIAS database. Results were compared with the semi-supervised state-of-the-art approaches BEMD, BMCS, Wavelet Analysis, LBI, Topographic Approach and MCW. Results show that our method achieves better results for circumscribed, spiculated lesions and ill-defined lesions, considering the similarity between segmentation results and ground-truth images.

Foreword: asynchronous behavior of cellular automata and discrete models

This special issue contains a selection of papers presented at the ‘‘Third International Workshop on Asynchronous Cellular Automata and Asynchronous Discrete Models’’ (ACA 2014), held as a satellite workshop of the 11th International Conference on Cellular Automata for Research and Industry (ACRI 2014) in Krakow (Poland) in September 2014. Six papers were selected and, after an additional review process, five of them have been included in this special issue. They are now presented in an extended and improved form with respect to the already refereed workshop version that appeared in the proceedings of the ACRI 2014 conference. The ACA workshop is devoted to the theme of asynchrony, a hot topic, inside Cellular Automata and other Discrete Models as, for instance, Boolean Networks. Cellular Automata are a well-known formal tool for modeling complex systems; they are considered in many scientific fields and industrial applications. Synchronicity is one of the main features of Cellular Automata evolutions. Indeed, in the most common Cellular Automata framework, all cells are updated simultaneously at each discrete time step by means of a same rule. Recent trends consider the modeling of asynchronous systems based on local and possibly non-uniform interactions. The aim of this workshop is to bring together researchers dealing with the theme of the asynchrony inside Cellular Automata and Discrete Models. Typical, but not exclusive, topics of the workshop are dynamics, complexity and computational issues, emergent properties, models of parallelism and distributed systems, and models of real phenomena. The paper ‘‘Local structure approximation as a predictor of second-order phase transitions in asynchronous cellular automata’’ by Henryk Fukś and Nazim Fates considers aasynchronous elementary cellular automata, that is elementary cellular automata in which each cell independently updates with probability a. By means of an extension of the mean-field approximation technique, the authors study the phase transitions in such automata, i.e., the changes of the dynamical behavior which may occur when the parameter a varies. In the paper ‘‘Supercritical probabilistic cellular automata: How effective is the synchronous updating?’’, PierreYves Louis deals with the issue of quantifying the effectiveness of the parallel updating in probabilistic cellular automata, i.e., cellular automata where the local rule is defined by means of a probability. Two interesting classes of probabilistic cellular automata are considered. An analysis of simulation is presented and shows that the behavior of these classes is nearly asynchronous when transition phase phenomena occur. Boolean Networks model the dynamical interaction of components which take a binary state. They have been & Alberto Dennunzio dennunzio@disco.unimib.it

The Transition Rules of 2D Linear Cellular Automata Over Ternary Field and Self-Replicating Patterns

In this paper we start with two-dimensional (2D) linear cellular automata (CA) in relation with basic mathematical structure. We investigate uniform linear 2D CA over ternary field, i.e. ℤ3. Present work is related to theoretical and imaginary investigations of 2D linear CA. Even though the basic construction of a CA is a discrete model, its macroscopic level behavior at large times and on large scales could be a close approximation to a continuous system. Considering some statistical properties, someone may also study geometrical aspects of patterns generated by cellular automaton evolution. After iteratively applying the linear rules, CA have been shown capable of producing interesting complex behaviors. Some examples of CA produce remarkably regular behavior on finite configurations. Using some simple initial configurations, the produced pattern can be self-replicating regarding some linear rules. Here we deal with the theory 2D uniform periodic, adiabatic and reflexive boundary CA (2D PB, AB and RB) over the ternary field ℤ3and the applications of image processing for patterns generation. From the visual appearance of the patterns, it is seen that some rules display sensitive dependence on boundary conditions and their rule numbers.

A novel estimator based learning automata algorithm

Reinforcement learning is one of the subjects of Artificial Intelligence and learning automata have been considered as one of the most powerful tools in this research area. On the evolution of learning automata, the rate of convergence is the most primary goal of designing a learning algorithm. In this paper, we propose a deterministic-estimator based learning automata (LA) of which the estimate of each action is the upper bound of a confidence interval, rather than the Maximum Likelihood Estimate (MLE) that has been widely used in current schemes of Estimator LA. The philosophy here is to assign more confidence on actions that are selected only for a few times, so that the automaton is encouraged to explore the uncertain actions. When all the actions have been fully explored, the automaton behaves just like the Generalized Pursuit Algorithm. A refined analysis is presented to show the 𝜖-optimality of the proposed algorithm. It has been demonstrated by extensive simulations that the presented learning automaton (LA) is faster than any deterministic estimator learning automata that have been reported to date. Moreover, we extend our algorithm to the stochastic estimator schemes. It is also shown that the extended LA has achieved a significant performance improvement, comparing with the current state of the art algorithm of learning automata, especially in complex and confusing environments.

Evolutionary Information Theory

Evolutionary information theory is a constructive approach that studies information in the context of evolutionary processes, which are ubiquitous in nature and society. In this paper, we develop foundations of evolutionary information theory, building several measures of evolutionary information and obtaining their properties. These measures are based on mathematical models of evolutionary computations, machines and automata. To measure evolutionary information in an invariant form, we construct and study universal evolutionary machines and automata, which form the base for evolutionary information theory. The first class of measures introduced and studied in this paper is evolutionary information size of symbolic objects relative to classes of automata or machines. In particular, it is proved that there is an invariant and optimal evolutionary information size relative to different classes of evolutionary machines. As a rule, different classes of algorithms or automata determine different information size for the same object. The more powerful classes of algorithms or automata decrease the information size of an object in comparison with the information size of an object relative to weaker4 classes of algorithms or machines. The second class of measures for evolutionary information in symbolic objects is studied by introduction of the quantity of evolutionary information about symbolic objects relative to a class of automata or machines. To give an example of applications, we briefly describe a possibility of modeling physical evolution with evolutionary machines to demonstrate applicability of evolutionary information theory to all material processes. At the end of the paper, directions for future research are suggested.

A Niche Cellular Genetic Algorithm Based on Evolution Rule

Since the individuals’ reproduction and existing probability can be greatly improved by introducing evolution rule into cellular automata algorithm, this paper presents a new niche cellular genetic algorithm by involving cellular automata evolution rule and introducing niche technique. The results of simulation experiments on some well-known complex benchmarking problems reveal that this algorithm outperforms the basic cellular genetic algorithms (cGAs) and cGAs with evolution rule in terms of diversity, convergence, global optimization and stability.

Evolutionary Automata: Expressiveness and Convergence of Evolutionary Computation

Expressiveness and convergence of evolutionary computation (EC) is studied using the evolutionary automata model. It turns out that all standard classes of evolutionary automata are equally expressive when they operate in the terminal mode, i.e. in the terminal mode, evolutionary finite automata (EFA) are as expressive as evolutionary pushdown automata, evolutionary linearly bounded automata, evolutionary Turing machines or evolutionary inductive Turing machines. For example, the simplest class of evolutionary automata, EFA, can accept all recursively enumerable languages (i.e. EFA have power of Turing machines) and even more—they can accept languages that are not recursively enumerable. Due to utilization of evolutionary automata, we obtain also very simple sufficient conditions for convergence of EC.

Study on Simulation Model of the Cellular Automaton for Passive Lane-Changing Based on the Avoidance of the Prospective Following Vehicle

It is the key fact for the accuracy of traffic simulation that the cellular automation model of traffic flow could simulate the real hybrid traffic flow. This article shows the method to improve cellular automaton model about two-lane hybrid vehicles based on passive lane-changing, to propose the avoidance rules about the prospective following vehicle, and to suggest the cellular automaton model and evolution rules based on the prospective following vehicle’s avoider. The simulation results show that the erroneous judgment rate for changing lane is the important facts for the state of two-lane hybrid traffic flow, and the accuracy of the simulation is improved in this article.

Finite State Automata Evolution Using Modular Architecture

The automatic creation of finite automata has long been a goal of the evolutionary computation community. The previous works in the evolution of finite state automata were limited to the evolution of strictly non-modular FSA. Here, a modular architecture to evolve FSA is proposed and a genetic programming procedure for evolving such structures is presented. Results on the Tomita Language benchmark indicate that the proposed procedure is indeed capable of successfully evolving modular FSA and that such modularity can result in a significantly increased rate of optimization.

Universality for Turing Machines, Inductive Turing Machines and Evolutionary Algorithms

The aim of this paper is the development of foundations for evolutionary computations. To achieve this goal, a mathematical model of evolutionary automata is introduced and studied. The main classe...

A stopped time dependent randomization cellular automata model for traffic flow controlled by traffic light

Modelling road traffic behavior using cellular automata has become a well-established method to analyze, understand, and even forecast the behavior of real road traffic, because the automata's evolution rules are simple, computationally efficient. In this paper, we presented a new model. In this model, the randomization probability is defined to be function of the stopped time of the vehicle: the longer the vehicle stops, the larger the randomization probability is. This means that the sensitivity of the drivers depends on the stopped time. The simulations show that although the fundamental diagram of the new model is similar to that of Nagel–Schreckenberg model, the saturated current depends on the cycle time of traffic light. We have explained the dependence of saturated current on cycle time and made the outlook of the future work. Our results indicate that we can adjust the cycle time of the traffic lights to enhance the road capacity.