Multiprocessor scheduling with communication delays

This paper adresses certain types of scheduling problems that arise when a parallel computation is to be executed on a multiprocessor. We define a model that allows for communication delays between precedence-related tasks, and propose a classification of various submodels. We also review complexity results and optimization and approximation algorithms that have been presented in the literature.     

Production scheduling and rescheduling with genetic algorithms

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable run-time costs.     

Two-dimensional patterns and images reconstruction with use of cellular automata

An approach based on an application of cellular automata (CA) to the problem of two-dimensional (2D) patterns or images reconstruction from ones with only partial information available is presented in the paper. 2D CA are used to process patterns/images, and genetic algorithm (GA) is applied to discover CA rules, which will be able to reconstruct original patterns/images from, e.g. destroyed or modified ones. A number of experiments have been conducted to reconstruct patterns and human face images with use of the proposed approach. Results of experiments show that CA rules discovered by GA in the learning process allow to reconstruct images with large number of damaged pixels.     

Multiprocessor task scheduling with resource requirements

One of the most important problems arising in multiprocessor systems is scheduling of tasks on a set of parallel processors. Recently, new models of task processing have been formulated in which certain tasks can require more than one processor at a time. This model is especially justified in some applications of multi-microprocessor systems. In this paper, we extend the above model to cover the case of scheduling in the presence of additional scarce resources. First a subcase of the problem is considered in which preemptable tasks need simultaneously one or two processors and one additional resource in the amount of one unit. For this case a low order polynomial-time algorithm is presented. Then the general case is solved via a linear programming approach.     

Search space reduction of asynchrony immune cellular automata

Natural Computing - We continue the study of asynchrony immunity in cellular automata (CA), which can be considered as a generalization of correlation immunity in the case of vectorial Boolean...     

基于遗传算法的多处理器系统任务调度

用一种遗传算法的调度策略,以大维度矩阵求逆为实验对象,探索在多核中如何完成任务的均衡分配问题,以达到加速效果.算法利用系统资源的弹性,自动搜寻可以并行的子任务并将其合理地分配到相应计算节点中,提高了多核系统资源调度性能,实现了对用户提交的任务的优化调度,达到了均衡系统各处理器计算负载和提高多核系统的总体性能的目标.     

Cryptography with cellular automata

Cryptography has become a basic requirement in this age of global electronic connectivity to secure data storage and transmission against the possibility of message eavsdropping and electronic fraud. In this article, we describe a single key crypto-graphic system based on one- and two-dimensional non-uniform cellular automata randomizers obtained by artificial evolution. The robustness of the scheme against cryptanalytic attacks is discussed and it is shown that direct cryptanalysis requires an exponentially growing amount of computational resources. The advantage of implementing the proposed scheme in hardware for high-speed operation is also discussed.     

An artificial immune system for solving production scheduling problems: a review

This article reviews the production scheduling problems focusing on those related to flexible job-shop scheduling. Job-shop and flexible job-shop scheduling problems are one of the most frequently encountered and hardest to optimize. This article begins with a review of the job-shop and flexible job-shop scheduling problem, and follow by the literature on artificial immune systems (AIS) and suggests ways them in solving job-shop and flexible job-shop scheduling problems. For the purposes of this study, AIS is defined as a computational system based on metaphors borrowed from the biological immune system. This article also, summarizes the direction of current research and suggests areas that might most profitably be given further scholarly attention.     

Linear cellular automata and Fischer automata

We study the sizes of minimal finite state machines associated with linear cellular automata. In particular, we construct a class of binary linear cellular automata whose corresponding minimal automata exhibit full exponential blow-up. These cellular automata have Hamming distance 1 to a permutation automaton. Moreover, the corresponding minimal Fischer automata as well as the minimal DFAs have maximal complexity. By contrast, the complexity of higher iterates of a cellular automaton always stays below the theoretical upper bound.     

A decision support system for the single-depot vehicle rescheduling problem

Disruptions in trips can prevent vehicles from executing their schedules as planned. Mechanical failures, accidents, and traffic congestion often hinder a vehicle schedule. When a vehicle on a scheduled trip breaks down, one or more vehicles need to be rescheduled to serve the passengers/cargo (if there are any) on that trip. The main objective of the vehicle rescheduling problem (VRSP) is to minimize operation and delay costs, while serving the passengers/cargo on the disrupted trip and completing all remaining trips that include the disrupted one. We report on a prototype decision support system (DSS) that recommends solutions for the single-depot rescheduling as well as vehicle scheduling (VSP) problems, since VRSP is closely related to VSP. The system was designed for human schedulers to obtain optimal vehicle assignments and reassignments. An experimental study, using randomly generated data, shows the efficiency of the developed algorithm. A real world problem, which involves the solid waste collection operational planning for a Brazilian city, is selected as the case study to illustrate the effectiveness of the developed DSS.     

Multiprocessor online scheduling of hard-real-time tasks

The problems of hard-real-time task scheduling in a multiprocessor environment are discussed in terms of a scheduling game representation of the problem. It is shown that optimal scheduling without a priori knowledge is impossible in the multiprocessor case even if there is no restriction on preemption owing to precedence or mutual exclusion constraints. Sufficient conditions that permit a set of tasks to be optimally scheduled at run time are derived     

Developing an artificial retina by evolutionary cellular automata and self-organizing neural networks

Retinas are very important for human beings to get information about their environment. In this paper, we propose a new method to build artificial retinas which have many features similar to real ones. We use evolutionary cellular automata to extract some basic characteristics of objects, and use self-organizing neural networks to distinguish different objects. The results indicate a way to get computer vision by artificial life. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, Janaury 19–21, 1998     

A cellular automata model of an anticipatory system

An anticipatory system has been modeled using the dynamic characteristics of cellular automata. Rules governing the steps in an enzymatic conversion of substrates to products are operative in the system. A concentration of an intermediate product influences the creation of a supplemental enzyme that enhances the competence of an enzyme down stream. This anticipation of the future event creates a condition in which the concentration of a later substrate is suppressed, a property characteristic of the system. The model presents a useful opportunity to study a variety of aspects of this fascinating phenomena.     

Multiprocessor task scheduling in multistage hybrid flowshops: A hybrid artificial bee colony algorithm with bi-directional planning

In this paper, the problem of scheduling multistage hybrid flowshops with multiprocessor tasks is contemplated. This is a strongly NP-hard problem for which a hybrid artificial bee colony (HABC) algorithm with bi-directional planning is developed to minimize makespan. To validate the effectiveness of the proposed algorithm, computational experiments were tested on two well-known benchmark problem sets. The computational evaluations manifestly support the high performance of the proposed HABC against the best-so-far algorithms applied in the literature for the same benchmark problem sets.     

The use of coevolution and the artificial immune system for ensemble learning

This paper presents two new approaches for constructing an ensemble of neural networks (NN) using coevolution and the artificial immune system (AIS). These approaches are extensions of the CLONal Selection Algorithm for building ENSembles (CLONENS) algorithm. An explicit diversity promotion technique was added to CLONENS and a novel coevolutionary approach to build neural ensembles is introduced, whereby two populations representing the gates and the individual NN are coevolved. The former population is responsible for defining the ensemble size and selecting the members of the ensemble. This population is evolved using the differential evolution algorithm. The latter population supplies the best individuals for building the ensemble, which is evolved by AIS. Results show that it is possible to automatically define the ensemble size being also possible to find smaller ensembles with good generalization performance on the tested benchmark regression problems. More interestingly, the use of the diversity measure during the evolutionary process did not necessarily improve generalization. In this case, diverse ensembles may be found using only implicit diversity promotion techniques.     

On real-time cellular automata and trellis automata

Summary It is shown that f(n)-time one-way cellular automata are equivalent to f(n)-time trellis automata, the real-time one-way cellular automata languages are closed under reversal, the 2n-time one-way cellular automata are equivalent to real-time cellular automata and the latter are strictly more powerful than the real-time one-way cellular automata.This work has been done during the second author's visit at the University of Paris and during both authors' visit at the Institute für Informationsverarbeitung Graz, Austria     

Symbiotic artificial immune system

Artificial Immune System algorithms use antibodies that fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm cannot make use of schemata or classes of partial solutions, while sub solutions can help a lot in faster emergence of a totally good solution in many problems. To exploit schemata in artificial immune systems, this paper presents a novel algorithm that combines traditional artificial immune systems and symbiotic combination operator. The algorithm starts searching with partially specified antibodies and gradually builds more and more specified solutions till it finds complete answers. The algorithm is compared with CLONALG algorithm on several multimodal function optimization and combinatorial optimization problems and it is shown that it is faster than CLONALG on most problems and can find solutions in problems that CLONALG totally fails.     

Image security system using recursive cellular automata substitution

This paper presents a novel image security system based on the replacement of the pixel values using recursive cellular automata (CA) substitution. This proposed image encryption method exhibits the properties of confusion and diffusion because of the characteristics of CA substitution are flexible. The salient features of the proposed image encryption method are its losslessness, symmetric private key encryption, very large number of secret keys, and key-dependent pixel value replacement. Simulation results obtained using some color and gray-level images clearly demonstrate the strong performance of the proposed image security system.     

Epidemiological modeling with a population density map-based cellular automata simulation system

We present a new numerical, two-dimensional cellular automata framework for simulation of the spread of an infectious disease in a region with non-homogenous spatial population distribution. For the simulation the real map of the population density in Poland is used, where the sources of the infection are located. Presented model is a combination of SEIR and IBM models complemented with additional factors like variable population density, death, birth and some stochastic parameters to reflect the more realistic population dynamics. In proposed model the states of individuals are tracked through time like in IBM model and the evolution of the whole system is described by SEIR transition function which determines how cells interact with their neighbours, influencing global behavior of the system. Presented model requires less complicated input than IBM models and is less expensive computationally. We explore influenza as the contagious disease in our map-based simulation. The results of the simulation show the spreading-rate of the disease and can be used to describe possible actions for preventing pandemic.