演化元胞自动机函数优化算法案例研究

BUMP是一个超多维,超多峰,超非线性的问题,被广泛应用于各种演化算法的性能比较。但最好解是未知的。基于元胞自动机的遗传算法报告了BUMP曾经发表过的最好解。该文设计了基于演化元胞自动机的新算法(ECAA)并获得了更好的结果。文中详细讨论了算法中各算子的设计方法及其在算法中扮演的角色,分析了该算法的极度并行,天然局部搜索等重要特性。     

A Parallel Skeletonization Algorithm Based on Two-Dimensional Cellular Automata and its VLSI Implementation

This paper presents a new parallel algorithm for binary object skeletonization and its implementation in VLSI. The proposed skeletonization algorithm is based on a digital geometry concept known as the 'Voronoi diagram, which is established, after an edge detection pre-processing step, through the time evolution of Cellular Automata. The proposed algorithm is both space and time efficient, since it does not require any distance calculations and ordering of distances as in the case of other skeletonization algorithms nor the application of complex 'template' criteria to guarantee object connectiveness as in the case of thinning algorithms. Additionally, the proposed Cellular Automaton architecture achieves high frequency of operation and it is especially suited for VLSI implementation due to its inherent parallelism, structural locality, regularity and modularity. A set of quantitative measures was defined in this paper in order to evaluate the performance of the proposed algorithm. It was found that the proposed algorithm for pattern skeletonization outperforms already existing algorithms, with respect to these measures.     

基于细胞自动机的交通流模拟控制

该文以JAVA语言为仿真工具,通过构造二维细胞自动机模型,对平面十字交叉路口的交通流进行了分析和模拟控制。仿真结果较为理想,这充分说明了细胞自动机极其广泛的应用前景。     

Genetic hybrid tuning of VARMAX and state space algorithms

The aim of the study was to monitor the system theoretic exogenous variables augmented state space algorithm of Aoki (State space modelling of time series. Springer, Heidelberg, 1987) and the VARMAX algorithm of Spliid (J Am Stat Assoc 78(384):843–849, 1983) within a geno-mathematical framework towards optimal parametric conditions/search intervals. Both algorithms were implemented as an integrated support library for a general computational platform, the Genetic Hybrid Algorithm (GHA), where some key parameters of the algorithms are defined in a search process utilizing a mixed geno-mathematical search technique. The empirical results of our tests using real economic data from the European stock market are encouraging. Specifically, the information criteria used in the VARMAX-search (Vector Autoregressive Moving Average algorithm with Exogenous variables) algorithm tend to favor parsimonious model representations automatically. Furthermore, the state space algorithm captures almost the same dynamics as the complex VARMAX-model estimated in the study. Both algorithms have encouraging in sample properties. When generating k-steps forecasts out-of-sample, k > 1, the state space algorithm seems to deteriorate faster than the VARMAX algorithm, however. The results suggest that more empirical testing is needed, especially in different situations with different degrees of model order and stationarity conditions, in order to provide more evidence on the suitability of the competing methods in particular cases. We demonstrated that the Genetic Hybrid Algorithm can be used as a generic platform for parametric search in vector valued time series modelling. Efficient procedures for optimal grouping of the individual time series processes and recognition of heteroskedasticity may improve the performance of the algorithms further.     

求解非线性方程组的元胞自动机方法及其全局收敛性证明

为了求得非线性方程组所有精确解,根据元胞自动机的特点构造了求解非线性方程组的全局收敛算法。在该算法中,将非线性方程组解的理论搜索空间划分为离散搜索空间,将离散搜索空间定义为元胞空间;离散搜索空间的每个点就是一个元胞,而一个元胞对应着非线性方程组的一个试探解;元胞的状态由其空间位置及位置修正量构成。将元胞空间划分为若干个非空子集,所有元胞的状态从一个非空子集转移到另一个非空子集的状态演化过程实现了元胞空间对理论搜索空间的搜索。在元胞状态演化过程中,元胞从一个状态转移到另一个状态的状态转移概率可以计算出来;元胞演化过程中的每个状态对应于有限Markov链上的一个状态。利用可归约随机矩阵的稳定性条件证明了该算法具有全局收敛性。仿真实例表明该算法是高效的。     

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

The continuous growth of computation power requirement has provoked computational Grids, in order to resolve large scale problems. Job scheduling is a very important mechanism and a better scheduling scheme can greatly improve the efficiency of Grid computing. A lot of algorithms have been proposed to address the job scheduling problem. Unfortunately, most of them largely ignore the security risks involved in executing jobs in such an unreliable environment as Grid. This is known as security problem and it is a main hurdle to make the job scheduling secure, reliable and fault-tolerant. In this paper, we present a Genetic Algorithm with multi-criteria approach, in terms of job completion time and security risks. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, proposing the Accelerated Genetic Algorithm. We also present the Accelerated Genetic Algorithm with Overhead which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristic algorithms. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.     

云计算下基于改进遗传算法的聚类融合算法

针对无监督聚类缺少数据分类等先验信息、基聚类的准确性受聚类算法影响以及一般聚类融合算法空间复杂度高的问题,提出一种基于改进遗传算法的聚类融合算法（CEIGA）;同时针对传统聚类融合算法已经不能满足大规模数据处理对于时间的要求的问题,提出一种云计算下使用Hadoop平台的基于改进遗传算法的并行聚类融合算法（PCEIGA）。首先,基聚类生成机制产生的基聚类划分在完成簇标签转化后进行基因编码作为遗传算法的初始种群。其次,通过改进遗传算法的选择算子,保证基聚类的多样性;再根据改进的选择算子对染色体进行交叉和变异操作并使用精英策略得到下一代种群,保证基聚类的准确性。如此循环,使聚类融合最终结果达到全局最优,提高算法准确度。通过设计两个MapReduce过程并加入Combine过程减少节点通信,提高算法运行效率。最后,在UCI数据集上比较了CEIGA、PCEIGA和四个先进的聚类融合算法。实验结果表明,与先进的聚类融合算法相比,CEIGA性能最好;而PCEIGA能在不影响聚类结果准确度的前提下明显降低算法运行时间,提高算法效率。     

基于元胞自动机的分布式数据挖掘分类器

针对常用分类方法分类精度较低和内存消耗较高的问题,设计一种基于多吸引子元胞自动机（MACA）的模式分类器tsPCM,把它应用于分布式数据挖掘。通过改变MACA的描述方法,用依赖串和依赖向量将分类过程设计成两阶段,用遗传算法优化设计。实验结果表明tsPCM具有较高的分类精度和较低的内存消耗,分类复杂度由O（n^3）降低到线性级O（n）,具有较好的应用价值。     

A combined heuristic optimization technique

Realistic problems of structural optimization are characterized by non-linearity, non-convexity and by continuous and/or discrete design variables. There are non-linear dependencies between the optimised parameters. Real-world problems are rarely decomposable or separable. In this contribution a combined heuristic algorithm is described which is well suited for problems, for which the application-requirements of gradient-based algorithms are not fulfilled. The present contribution describes a combination of the Threshold Accepting Algorithm with Differential Evolution with particular emphasis on structural optimization, it can be classified as a Hybrid Evolutionary Algorithm. The Threshold Accepting Algorithm is similar to Simulated Annealing. Differential Evolution is based on Genetic Algorithms.     

Achieving super-linear performance in parallel multi-objective evolutionary algorithms by means of cooperative coevolution

This article introduces three new multi-objective cooperative coevolutionary variants of three state-of-the-art multi-objective evolutionary algorithms, namely, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Multi-objective Cellular Genetic Algorithm (MOCell). In such a coevolutionary architecture, the population is split into several subpopulations or islands, each of them being in charge of optimizing a subset of the global solution by using the original multi-objective algorithm. Evaluation of complete solutions is achieved through cooperation, i.e., all subpopulations share a subset of their current partial solutions. Our purpose is to study how the performance of the cooperative coevolutionary multi-objective approaches can be drastically increased with respect to their corresponding original versions. This is specially interesting for solving complex problems involving a large number of variables, since the problem decomposition performed by the model at the island level allows for much faster executions (the number of variables to handle in every island is divided by the number of islands). We conduct a study on a real-world problem related to grid computing, the bi-objective robust scheduling problem of independent tasks. The goal in this problem is to minimize makespan (i.e., the time when the latest machine finishes its assigned tasks) and to maximize the robustness of the schedule (i.e., its tolerance to unexpected changes on the estimated time to complete the tasks). We propose a parallel, multithreaded implementation of the coevolutionary algorithms and we have analyzed the results obtained in terms of both the quality of the Pareto front approximations yielded by the techniques as well as the resulting speedups when running them on a multicore machine.     

Implementation and comparison of algorithms for multi-objective optimization based on genetic algorithms applied to the management of an automated warehouse

The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. Genetic algorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto Genetic Algorithm, the Non-dominated Sorting Genetic Algorithm 2 and the Strength Pareto Genetic Algorithm 2, are described in details and the achieved results are widely discussed; moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.     

Predictive reference shaping for constrained robotic systems using evolutionary algorithms

This paper proposes a method for reducing the trajectory tracking errors of robotic systems in presence of input saturation and state constraints. Basing on a finite horizon prediction of the future evolution of the robot dynamics, the proposed device online preshapes the reference trajectory, minimizing a multi-objective cost function. The shaped reference is updated at discrete time intervals and is generated taking into account the full nonlinear robot dynamics, input and state constraints. A specialized Evolutionary Algorithm is employed as search tool for the online computation of a sub-optimal reference trajectory in the discretized space of the control alternatives. The effectiveness of the proposed method and the online computational burden are analyzed numerically in two significant robotic control problems; furthermore a comparison of the performance provided by this method and an iterative gradient-based algorithms are discussed.     

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms—a Genetic Algorithm and a Memetic Algorithm with local search—are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.     

Genetic diversity as an objective in multi-objective evolutionary algorithms

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.     

A Cellular Automaton Processor for Line and Corner Detection in Gray-Scale Images

The design and VLSI implementation of a Cellular Automaton processor for the detection of lines and corners in gray-scale images is presented in this paper. The behavior of a number of different Cellular Automaton rules was investigated and it was found that certain rules result in transitions in the Cellular Automaton state-transition diagram that correspond to the masks required for the line and corner detection. More specifically, the one-dimensional Cellular Automaton of length 8, operating under rule 56 with periodic boundary conditions, is capable of generating different sets of mask operators for line detection, corner detection and dominant point detection (and, thus, for arbitrarily-shaped curve detection), depending only on the initial state of the Cellular Automaton, without any additional hardware cost for the implementation or the reconfiguration of different masks. The proposed architecture was designed and implemented on a single VLSI chip using 0.7 μm double-layer metal (DLM) CMOS technology. The behavior of the chip was successfully verified for all sets of masks for line detection, corner detection and dominant point detection.     

Structured population genetic algorithms: a literature survey

The Genetic Algorithm (GA) has been one of the most studied topics in evolutionary algorithm literature. Mimicking natural processes of inheritance, mutation, natural selection and genetic operators, GAs have been successful in solving various optimization problems. However, standard GA is often criticized as being too biased in candidate solutions due to genetic drift in search. As a result, GAs sometimes converge on premature solutions. In this paper, we survey the major advances in GA, particularly in relation to the class of structured population GAs, where better exploration and exploitation of the search space is accomplished by controlling interactions among individuals in the population pool. They can be classified as spatial segregation, spatial distance and heterogeneous population. Additionally, secondary factors such as aging, social behaviour, and so forth further guide and shape the reproduction process. Restricting randomness in reproduction has been seen to have positive effects on GAs. It is our hope that by reviewing the many existing algorithms, we shall see even better algorithms being developed.     

基于细胞自动机的安全图像加密算法

利用初等细胞自动机状态环性质进行加密后的密文图像仍可看出原图像的部分轮廓,且明文敏感性差。由此提出一种安全的图像加密算法,采用周期为2的二维细胞自动机对图像进行位平面加密,再通过状态环加密的方法使明文改变1 bit即可影响整个密文图像。仿真结果表明,改进算法敏感性更高,密钥空间更大,且加密速度较快。     

基于空间信息多级网格的CA模型研究

具备时空计算特征的元胞自动机(CA)模型与GIS集成极大促进了GIS对地理过程的模拟能力。论文简要介绍了空间信息多级网格(SIMG)——一种既能适合网格计算环境又充分考虑到地球空间的自然特征和社会属性的差异性及经济发展不平衡的特点的空间信息表示新方法。充分研究了SIMG与CA之间的联系,分别讨论了在SIMG上CA元胞及状态的确定、元胞空间的确定、规则的定义、时间粒度确定等,提出了空间信息多级网格元胞自动机模型(SIMGCA),并提出了SIMGCA模型在土地利用/覆被变化中的应用框架。     

Two-dimensional packing problems using genetic algorithms

This paper presents a technique for applying genetic algorithms for the two-dimensional packing problem. The approach is applicable to not only convex shaped objects, but can also accommodate any type of concave and complex shaped objects including objects with holes. In this approach, a new concept of a two-dimensional genetic chromosome is introduced. The total layout space is divided into a finite number of cells for mapping it into this 2D genetic algorithm chromosome. The mutation and crossover operators have been modified and are applied in conjunction with connectivity analysis for the objects to reduce the creation of faulty generations. A new feature has been added to the Genetic Algorithm (GA) in the form of a new operator called compaction. Several examples of GA-based layout are presented.