Emergence of intelligence in next-generation manufacturing systems

In the paper we propose a fundamental shift from the present manufacturing concepts and problem solving approaches towards new manufacturing paradigms involving phenomena such as emergence, intelligence, non-determinism, complexity, self-organization, bottom-up organization, and coexistence with the ecosystem. In the first part of the paper we study the characteristics of the past and the present manufacturing concepts and the problems they caused. According to the analogy with the terms in cognitive psychology four types of problems occurring in complex manufacturing systems are identified. Then, appropriateness of various intelligent systems for solving of these four types of problems is analyzed. In the second part of the paper, we study two completely different problems. These two problems are (1) identification of system in metal forming industry and (2) autonomous robot system in manufacturing environment. A genetic-based approach that imitates integration of living cells into tissues, organs, and organisms is used. The paper clearly shows how the state of the stable global order (i.e., the intelligence) of the overall system gradually emerges as a result of low-level interactions between entities of which the system consists and the environment.     

Genotype representations in grammatical evolution

Grammatical evolution (GE) is a form of grammar-based genetic programming. A particular feature of GE is that it adopts a distinction between the genotype and phenotype similar to that which exists in nature by using a grammar to map between the genotype and phenotype. Two variants of genotype representation are found in the literature, namely, binary and integer forms. For the first time we analyse and compare these two representations to determine if one has a performance advantage over the other. As such this study seeks to extend our understanding of GE by examining the impact of different genotypic representations in order to determine whether certain representations, and associated diversity-generation operators, improve GE’s efficiency and effectiveness. Four mutation operators using two different representations, binary and gray code representation, are investigated. The differing combinations of representation and mutation operator are tested on three benchmark problems. The results provide support for the use of an integer-based genotypic representation as the alternative representations do not exhibit better performance, and the integer representation provides a statistically significant advantage on one of the three benchmarks. In addition, a novel wrapping operator for the binary and gray code representations is examined, and it is found that across the three problems examined there is no general trend to recommend the adoption of an alternative wrapping operator. The results also back up earlier findings which support the adoption of wrapping.     

几种人类生物网络的自相似性实证研究

介绍了复杂网络自相似性的基本概念,阐述了自相似指数及其计算方法盒覆盖算法的基本原理和方法,并对四种不同类型的人类生物网络进行了实证分析。结果表明,无论是否考虑流通代谢物和小分子代谢物,新陈代谢网络都是自相似的,但是其他的生物网络大多不是自相似的。     

Brave Mobots Use Representation: Emergence of Representation in Fight-or-Flight Learning

The paper uses ideas from Machine Learning, Artificial Intelligence and Genetic Algorithms to provide a model of the development of a fight-or-flight response in a simulated agent. The modelled development process involves (simulated) processes of evolution, learning and representation development. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of structures which can be given a representational interpretation. It also shows how these may form the infrastructure for closely-coupled agent/environment interaction.     

区域能量和图像自相似的NSCT域图像融合

非降采样轮廓波变换(NSCT)不仅具有平移不变性,还拥有足够的冗余信息,可以更有效地提取源图像中的方向信息,使得融合后的图像更符合人眼的视觉特性.利用NSCT在图像处理中的优势,针对红外光与可见光图像的融合,提出了一种新的基于改进的区域能量和图像自相似的NSCT域图像融合算法.首先,利用NSCT将图像分解为一个低频子带和多个不同方向的带通子带;然后,对低频子带采用区域能量自适应加权融合规则,带通方向子带则利用图像自相似进行系数的融合;最后,对融合系数进行NSCT逆变换,以重构生成融合图像.实验结果表明:与一些现有算法相比,该算法的图像融合结果在人眼主观视觉标准和客观评价标准上均具有明显优势.     

Emergence of Orienting Behavior in Ecological Neural Networks

We investigated the emergence of orienting behavior in artificial organisms that evolved following a genetic algorithm. These organisms live in a simulated environment containing food and danger elements and reproduce selectively based on the capacity of each individual to eat food while avoiding danger. When the amount of computational resources (number of hidden units) is adequate to the difficulty of the perceptual discrimination between food and danger, peripheral vision is sufficient to trigger stimulus identification. When the resources are scarce, the central portion of the sensory surface becomes a ‘fovea’, and the presence of a stimulus in peripheral vision triggers an orienting movement (foveation), before the organism can decide whether to eat or to avoid the object. Thus, orienting movements, as well as the segregation of processing resources into a high-definition fovea and a poor-definition periphery, may originate from a disproportion between complex perceptual tasks and (relatively) scarce computational resources. This revised version was published online in August 2006 with corrections to the Cover Date.     

带子群自组织蠕虫算法及其在多模态问题中的应用

提出了一种全新的多模态遗传算法一带子群的自组织蠕虫算法（SSOMA）。该算法的基本恿想是：通过初始群体的前期寻优找到峰值点的所在邻域;随后分别在这些邻域内选择少量的个体组成子群,在这些子群中再利用自组织蠕虫算法进行后期寻优,从而找到所有的峰值点,该算法极大地降低了计算的复杂度、提高了收敛速度。最后,用经典测试函数对该算法进行了仿真实验,并进行了计算复杂度分析,结果表明该算法在多模态函数优化方面具有较为理想的应用前景。     

自适应遗传算法在车牌定位中的应用

车牌自动识别是现代智能交通的重要组成部分,而车牌定位技术又是车牌识别系统的核心之一。由于传统的定位算法存在适应性差,鲁棒性不强的问题,提出一种基于自适应遗传算法的车牌定位方法。先用最大类间方差法(OTSU)对车牌图像进行二值化,然后利用遗传算法对全图进行车牌特征匹配搜索,结合区域特征向量构造的适应度函数,最终找到车牌区域的最佳定位参量。测试结果表明,该算法适应性强,定位效果很好。     

遗传算法及其并行性研究

遗传算法是一种解决优化问题的方法。经过多年的发展,已经在很多领域取得了成功的应用。而传统的遗传算法经过改进,产生了各种改进后的遗传算法,而并行性是遗传算法改进的重点突破方向,重点研究了遗传算法的并行性。并结合项目开发出实例。     

基于混沌遗传算法的PID参数优化

随着计算机技术的飞跃发展和人工智能技术渗透到自动控制领域,各种先进PID控制器参数整定方法层出不穷,给PID控制器参数整定的研究带来了无限活力和契机;然而很多先进的PID参数整定方法并没有像预期的那样产生完美的控制效果.将遗传算法和混沌优化方法智能集成,利用混沌序列的遍历性、随机性、规律性的特点生成初始种群,在遗传操作中加入混沌细搜索,大大提高了局部搜索能力,能有效防止遗传算法陷入局部最优和发生早熟现象,仿真表明,混沌遗传算法优化结果相当理想,效果令人满意,优于常规的遗传算法.     

混合遗传算法及与标准遗传算法对比研究

文章详尽地阐述了1996年Pham和Jin提出的一种运用改进繁殖机制的遗传模型,称为混合遗传算法(HGA,HybridGeneticAlgorithm),并对HGA和GA(GeneticAlgorithm)的效能进行了对比性分析。理论与实验结果表明混合遗传算法收敛性明显快于标准遗传算法。     

The path and location planning of workpieces by genetic algorithms

This paper presents the planning of a near-optimum path and location of a workpiece by genetic algorithms. The purpose of this planning is to minimize the processing time required for a robot to complete its work on a workpiece. The location of the workpiece can be anywhere by translating it along any direction and by rotating it about the fixedz-axis of the robot coordinate system. Owing to the changeable location of the workpiece and the alterable motion time required for a robot to move between two workpoints, the path and location planning problem is much more complicated than the travelling salesman problem. It is definitely impossible to obtain an optimum path and location within an acceptable time. In this paper, genetic algorithms are applied to solve this problem. The location of the workpiece is defined by three position parameters and one angular parameter, and the path is determined based on the values of the parameters for all workpoints. All the path and location parameters are encoded into a binary string. They are modified simultaneously by genetic algorithms to search for a global solution. As the workpiece can be anywhere, a penalty function is used to prevent the selection of illegal paths. Two experiments are given to show the performance of genetic algorithms: one has 30 workpoints and the other has 50 workpoints. Compared with four human-generated plannings, planning by genetic algorithms has much better performance in minimizing the processing time.     

Economics of Reciprocal Networks: Collaboration in Knowledge and Emergence of Industrial Clusters

Collaboration in knowledge gives rise to new patterns of competition, since knowledge is created with cost in an organization but is transferred without additional cost under a collaborative agreement. Simulation analysis based on a specific market network model successfully explains the emergence of industrial clusters under this assumption. When a significant number of markets are connected to form a network, Cluster Competition leads to uneven production levels in each market. An industrial cluster emerges even if markets are evenly connected with identical conditions. This work also shows that a Free Scale Network of markets results in extreme divergence of production levels in each node. The conditions of each market are the same ex ante, but one fortunate market grows to produce a huge volume of production for the entire network ex post. This effect acts as an invisible hand to create uneven development of economic locations.     

Performance evaluation of a two stage adaptive genetic algorithm (TSAGA) in structural topology optimization

Genetic algorithm with island and adaptive features has been used for reaching the global optimal solution in the context of structural topology optimization. A two stage adaptive genetic algorithm (TSAGA) involving a self-adaptive island genetic algorithm (SAIGA) for the first stage and adaptive techniques in the second stage is proposed for the use in bit-array represented topology optimization. The first stage, consisting a number of island runs each starting with a different set of random population and searching for better designs in different peaks, helps the algorithm in performing an extensive global search. After the completion of island runs the initial population for the second stage is formed from the best members of each island that provides greater variety and potential for faster improvement and is run for a predefined number of generations. In this second stage the genetic parameters and operators are dynamically adapted with the progress of optimization process in such a way as to increase the convergence rate while maintaining the diversity in population. The results obtained on several single and multiple loading case problems have been compared with other GA and non-GA-based approaches, and the efficiency and effectiveness of the proposed methodology in reaching the global optimal solution is demonstrated.     

网格环境下改进的独立任务调度遗传算法

针对网格环境下独立任务的调度问题,提出了一种新的混合遗传算法,通过调整算法结构,来增加染色体的多样性,通过加入针对特定问题的调整操作,来有效地提高算法的局部搜索能力,使遗传算法兼具全局和局部搜索能力,防止早熟收敛。仿真实验表明,跟其他算法相比,提出的算法取得了很好的调度长度,并且收敛速度也很快。     

基于免疫遗传算法的前向神经网络设计

针对传统BP算法训练速度慢、易陷入局部最优等缺点,该文提出了一种采用免疫遗传算法设计前向神经网络的方法。为解决神经网络权值随机初始化带来的问题,介绍了一种基于免疫的多样性模拟退火法(SAND算法)来进行神经网络权值初始化。仿真结果表明,该算法比混合遗传算法有更高的性能。     

Solving very large instances of the scheduling of independent tasks problem on the GPU

In this paper, we present two new parallel algorithms to solve large instances of the scheduling of independent tasks problem. First, we describe a parallel version of the Min–min heuristic. Second, we present GraphCell, an advanced parallel cellular genetic algorithm (CGA) for the GPU. Two new generic recombination operators that take advantage of the massive parallelism of the GPU are proposed for GraphCell. A speedup study shows the high performance of the parallel Min–min algorithm in the GPU versus several CPU versions of the algorithm (both sequential and parallel using multiple threads). GraphCell improves state-of-the-art solutions, especially for larger problems, and it provides an alternative to our GPU Min–min heuristic when more accurate solutions are needed, at the expense of an increased runtime.     

光纤通信网络中信号再生器定位问题的求解

信号再生器定位问题是光纤通信网络中一种新型组合优化问题,该问题的求解质量直接影响通信网络的设计成本,并且理论上被证明是NP完全问题.给出一种基于反向学习的有偏随机键遗传算法的求解方法,在有偏随机键遗传算法的基础上采用反向学习策略,充分挖掘反向解中的优化信息,提高算法的全局搜索能力.对大量标准测试算例的计算表明算法具有良好的优化性能,又通过与有偏随机键遗传算法和人工协作搜索算法的比较,进一步说明了本算法的高效性.     

微震定位系统中拾震器布阵研究

对微震监测中拾震器阵列进行科学有效的布置是保证微震定位系统能有效运行的一个关键条件,设计了4种拾震器阵列布设方案,使煤矿中微震定位监测系统运行得更加科学有效.首先建立复杂地质模型,利用微变网格射线追踪正演模拟出微震源起震到拾震器阵列接收信号的过程,然后在施加随机扰动的情况下,利用粒子群-神经网络算法(PSO-BP)反演推算出震源位置,最后通过震源位置的误差来分析各个布阵方案的特点,同时分析了阵列中拾震器数量的合理值.研究结果表明,在微震监测系统中拾震器利用率最高的情况下,可以给出准确、合理、有效的拾震器布阵方案.     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.