Foreword: cellular automata and applications

International audienceThis special issue contains four papers presented during theworkshop, ‘‘18th International Workshop on CellularAutomata and Discrete Complex Systems’’ (Automata2012), held in La Marana, Corsica island (France) in theperiod September 19–21th, 2012.The aim of this workshop is to establish and maintain apermanent, international, multidisciplinary forum for thecollaboration of researchers in the field of Cellular Automata(CA) and Discrete Complex Systems (DCS), providea platform for presenting and discussing new ideas andresults, and support the development of theory and applicationsof CA and DCS.Typical, but not exclusive, topics of the workshop are:dynamics aspects, algorithmic, computational and complexityissues, emergent properties, formal language processing,models of parallelism and distributed systems,phenomenological descriptions, scientific modeling andpractical applications.After an additional review process, four papers wereselected and included in this special issue. They are nowpresented in an extended and improved form with respectto the already refereed workshop version that appeared inthe proceedings of Automata 2012.The paper ‘‘Computation of Functions on n Bits byAsynchronous Clocking Cellular Automata’’ by MichaelVielhaber aims at proving that different functions on binaryvectors can be computed by changing the updating schemefrom a fully synchronous to an asynchronous one on somefixed CA local rule.In their paper ‘‘Solving the Parity Problem in One–Dimensional Cellular Automata’’, Heather Betel, PedroP. B. de Oliveira, and Paola Flocchini deal with the parityproblem in one–dimensional cellular automata (CA): a CAlocal rule solves the parity problem if, starting from anyinitial configuration, the CA converges to the 0–configuration(resp., the 1–configuration) if and only if the initialconfiguration contains an even number of 1s (resp., an oddnumber of 1s). In particular, authors focus on the neighborhoodsize of CA rules solving the problem.Murillo G. Carneiro and Gina M. B. Oliveira present inthe paper ‘‘Synchronous Cellular Automata-Based Schedulerinitialized by Heuristic and modeled by a Pseudolinearneighborhood’’ two approaches based on CA to thetask scheduling problem in multiprocessor systems.The implementation of cellular automata on processorarrays is considered by Jean-Vivien Millo and Robertde Simone in the paper ‘‘Explicit routing schemes forimplementation of cellular automata on processor arrays’’.They deal with the trade-offs between the generality of theCA neighborhood and the limited expressive power providedby physical platforms. This is an extremely hot topicwhich will help in turning CA towards real extendedapplications.We would like to warmly thank the authors for theirwork and effort which made this special issue possible.Special thanks go to all referees for their valuable contributionsboth during the selection and the final reviewprocess. Finally, we also want to thank Professor GrzegorzRozenberg for offering us the opportunity to publish thisspecial issue in Natural Computing

Image reconstruction with the use of evolutionary algorithms and cellular automata

In the paper we present a new approach to the image reconstruction problem based on evolution algorithms and cellular automata. Two-dimensional, nine state cellular automata with the Moore neighbourhood perform reconstruction of an image presenting a human face. Large space of automata rules is searched through efficiently by the genetic algorithm (GA), which finds a good quality rule. The experimental results show that the obtained rule allows to reconstruct an image with even 70% damaged pixels. Moreover, we show that the rule found in the genetic evolution process can be applied to the reconstruction of images of the same class but not presented during the evolutionary one

Simulation techniques in an artificial society model

Artificial society refers to a generic class of agent-based simulation models used to discover global social structures and collective behavior produced by simple local rules and interaction mechanisms. Artificial society models are applicable in a variety of disciplines, including the modeling of chemical and biological processes, natural phenomena, and complex adaptive systems. We focus on the underlying simulation techniques used in artificial society discrete-event simulation models, including model time evolution and computational performance.;Although for some applications synchronous time evolution is the correct modeling approach, many other applications are better represented using asynchronous time evolution. We claim that asynchronous time evolution can eliminate potential simulation artifacts produced using synchronous time evolution. Using an adaptation of a popular artificial society model, we show that very different output can result based solely on the choice of asynchronous or synchronous time evolution. Based on the event list implementation chosen, the use of discrete-event simulation to incorporate asynchronous time evolution can incur a substantial loss in computational performance. Accordingly, we evaluate select event list implementations within the artificial society simulation model and demonstrate that acceptable performance can be achieved.;In addition to the artificial society model, we show that transforming from a synchronous to an asynchronous system proves beneficial for scheduling resources in a parallel system. We focus on non-FCFS job scheduling policies that permit jobs to backfill, i.e., to move ahead in the queue, given that they do not delay certain previously submitted jobs. Instead of using a single queue of jobs, we propose a simple yet effective backfilling scheduling policy that effectively separates short from long jobs by incorporating multiple queues. By monitoring system performance, our policy adapts its configuration parameters in response to severe changes in the job arrival pattern and/or resource demands. Detailed performance comparisons via simulation using actual parallel workload traces indicate that our proposed policy consistently outperforms traditional backfilling in a variety of contexts

Thermal aware task assignment for multicore processors using genetic algorithm

Microprocessor power and thermal density are increasing exponentially. The reliability of the processor declined, cooling costs rose, and the processor's lifespan was shortened due to an overheated processor and poor thermal management like thermally unbalanced processors. Thus, the thermal management and balancing of multi-core processors are extremely crucial. This work mostly focuses on a compact temperature model of multicore processors. In this paper, a novel task assignment is proposed using a genetic algorithm to maintain the thermal balance of the cores, by considering the energy expended by each task that the core performs. And expecting the cores’ temperature using the hotspot simulator. The algorithm assigns tasks to the processors depending on the task parameters and current cores’ temperature in such a way that none of the tasks’ deadlines are lost for the earliest deadline first (EDF) scheduling algorithm. The mathematical model was derived, and the simulation results showed that the highest temperature difference between the cores is 8 °C for approximately 14 seconds of simulation. These results validate the effectiveness of the proposed algorithm in managing the hotspot and reducing both temperature and energy consumption in multicore processors