人工生命算法的研究进展

人工生命算法(ALA)是一种新兴的演化算法,利用人工生命群体的突现聚类实现全局寻优.近些年来,对基于人工生命系统的新型分布式算法进行了研究,算法的应用潜力在多个领域得到展现.算法不需要梯度信息,可用于多个类型的优化领域.本文对人工生命算法的研究进展进行了较为详细的介绍,并且对今后的研究方向进行了展望.     

人工生命算法综述

介绍了人工生命算法的基本原理,改进措施,研究现状,以及对未来研究的展望。人工生命算法是一种新兴的优化技术,其思想来源于达尔文进化论。利用人工生命的突现行为及人工生命可以动态地和环境相互作用的特点,实现全局寻优,在优化非线性连续多模态函数时,具有良好的全局收敛性。人工生命算法已经吸引了许多研究领域的关注,它是一种分布式优化技术,算法采用自下而上的设计方法,不需要梯度信息,为优化技术提供了一种新思路。     

人工生命与人工生命计算

论文从人工生命的角度定义了人工生命计算的概念,提出了人工生命计算的一般框架。人工生命计算是一种以人工生命为形式、研究人工生命的信息表达和处理机制,提取相应的计算模型,嵌入相应的计算方法模拟自然界生物进化过程与信息处理机制来求解与优化问题的智能计算方法。同时对人工生命计算的理论基础包括遗传算法、人工神经网络、自动机理论、L-系统、智能体和多智能体系统和计算生态学等进行了概述;并对两种典型的人工生命计算方法进行了初步的研究。最后说明了人工生命计算的特点及目前的应用领域。人工生命计算具有非常显著的特点和优点,在科学和工程的诸多实际应用领域具有广泛的应用前景。     

人工生命浅谈

1987年兰顿首次提出了人工生命的概念,定义“人工生命是研究人工系统来模拟自然生命系统行为特性的学科”,经过20年的发展,人工生命的独立学科地位已经得到国际学术界的广泛承认,正在逐渐成为学术研究的热点之一,人工生命的研究兴趣在于对生命系统行为特性的仿生,学科中应使用由下而上合成的方法,以使人工系统具有很好适应性、灵活性。本文将介绍人工生命起源、重要概念和发展趋势等方面的内容。     

生命的仿真--人工生命

介绍了人工生命学科产生的背景、发展历程以及C．Langton关于人工生命的定义、研究内容和研究方法，重点介绍了人工生命中自发“突现”的概念。对C．Langton的主要学术观点进行了归纳与述评，系统总结了人工生命研究领域的主要研究成果。指出人工生命学科研究类似生命行为和现象的发生及其信息处理机制，其成果在工程技术领域有良好应用前景。     

广义人工生命的科学基础(II)——生物工程基础

广义人工生命是自然生命的模拟、延伸和扩展。广义的人工生命包括转基因动物和克隆动物,因此动物转基因技术和动物的克隆技术是人工生命的技术基础。该文首先给出了转基因动物和克隆动物的定义,对动物转基因技术、动物的克隆技术和它们之间的关系做了探讨和扼要评述。最后给出了广义人工生命的科学基础框架。     

具有行为强化学习能力的人工生命进化模型设计

人工生命进化模型设计的关键问题是学习与进化之间的关系,在自主体生存期内的学习过程可以通过不同的遗传方式指导个体行为的进化。该文利用进化算法和人工神经网络的研究方法,设计了两种不同的人工生命自主体的进化模型,模型解决了先天的遗传进化和后天的个体神经系统强化学习的有机结合问题,并且得出结论认为,强化学习有助于自主体适应复杂的外部环境,同时学习也可以直接或间接地使该适应性成为自主体遗传基因上的固定成分。     

广义人工生命的科学基础(Ⅱ) --生物工程基础

广义人工生命是自然生命的模拟、延伸和扩展。广义的人工生命包括转基因动物和克隆动物，因此动物转基因技术和动物的克隆技术是人工生命的技术基础。该文首先给出了转基因动物和克隆动物的定义，对动物转基因技术、动物的克隆技术和它们之间的关系做了探讨和扼要评述。最后给出了广义人工生命的科学基础框架。     

抛物型方程反问题的混合粒子群算法

粒子群优化算法（PSO）是一种新兴的优化技术,它的思想来源于人工生命和演化计算理论。PSO通过粒子追随自己找到的最好解和整个群的最好解来完成优化。粒子群算法简单容易实现,可调参数少,已经得到了广泛研究和应用。提出了一种结合有限元方法求解偏微分方程反问题的混合粒子群算法,在对多个抛物型方程反问题模型测试的数值模拟中都得到了较好的结果,体现了该算法的有效性、通用性和稳健性。     

群体智能与人工生命

群体智能和人工生命是正在迅速发展的新兴研究领域.它们通过对自然界生命现象的模拟,在不同层次上揭示生命现象和进化规律,为复杂系统的复杂行为建模与仿真提供了新的思路.本文分别对人工生命和群体智能进行了综述,分析了两者的区别与联系,并对人工生命和群体智能的未来发展趋势进行了展望.     

Relay Reachability Algorithm for Exploring Huge State Space

Since many desirable properties about finite-state model are expressed as a reachability problem, reachability algorithms have been extensively studied in model checking. On the other hand, reachability algorithms play an important role in game solving since reachability games are often described as a finite state model. In this sense, reachability algorithms are located in the intersection of the research areas of Model Checking and Artificial Intelligence.This paper interests in solving the reachability games called Push-Push. However, both exact and approximate reachability algorithms are not sufficient to the games since its state space is huge and requires lots of iterations such as 338 steps in the reachability computation. Thus we devise the new algorithm called relay reachability algorithm. It divides the global state space into several local ones. And exact reachability algorithm is applied on each local state space one by one. With these reachability algorithms, we solve all of the games.     

Artificial Animals and Humans: From Physics to Intelligence

The confluence of virtual reality and artificial life, an emerging discipline that spans the computational and biological sciences, has yielded synthetic worlds inhabited by realistic, artificial flora and fauna. Artificial animals are complex synthetic organisms that possess functional biomechanical bodies, sensors, and brains with locomotion, perception, behavior, learning, and cognition centers. Artificial humans and other animals are of interest in computer graphics because they are self‐animating characters that dramatically advance the state of the art of production animation and interactive game technologies. More broadly, these biomimetic autonomous agents in their realistic virtual worlds also foster deeper, computationally oriented insights into natural living systems.     

人工生命

人工生命是指用计算机和精密机械等生成或构造表现自然生命系统行为特点的仿真系统或模型系统，自然生命系统的行为特点表现为自组织，自修复，自复制的基本性质，以及形成这些性质的混沌动力学，环境适应和进化，本文主要介绍人工生命研究的原因，发展过程，模型，讨论人工生命研究的方法，基础理论，以及人工生命研究的前景。     

Artificial life techniques for load balancing in computational grids

Load balancing is a very important and complex problem in computational grids. A computational grid differs from traditional high performance computing systems in the heterogeneity of the computing nodes and communication links, as well as background workloads that may be present in the computing nodes. There is a need to develop algorithms that could capture this complexity yet can be easily implemented and used to solve a wide range of load balancing scenarios. Artificial life techniques have been used to solve a wide range of complex problems in recent times. The power of these techniques stems from their capability in searching large search spaces, which arise in many combinatorial optimization problems, very efficiently. This paper studies several well-known artificial life techniques to gauge their suitability for solving grid load balancing problems. Due to their popularity and robustness, a genetic algorithm (GA) and tabu search (TS) are used to solve the grid load balancing problem. The effectiveness of each algorithm is shown for a number of test problems, especially when prediction information is not fully accurate. Performance comparisons with Min-min, Max-min, and Sufferage are also discussed.     

对精英加速的改进人工鱼群算法*

人工鱼群算法是一种群智能全局随机优化算法,存在算法收敛精度低和效率差的缺点。为克服这一缺点,利用最速下降法具有运算简单、运算速度较快的特点,提出了对精英加速的改进人工鱼群算法。该算法利用最速下降法对适应度值最好的人工鱼更新,通过人工鱼之间信息交换指导其他人工鱼,提高鱼群整体水平,加快人工鱼群算法收敛速度。数值试验结果表明,所得改进人工鱼群算法不仅运算量减少,而且具有更快的收敛速度和更高的收敛精度。改进算法提高收敛精度和运算效率,相较其他算法具有一定优势。     

A unified architecture for agent behaviors with selection of evolved neural network modules

To model complex systems for agent behaviors, genetic algorithms have been used to evolve neural networks which are based on cellular automata. These neural networks are popular tools in the artificial life community. This hybrid architecture aims at achieving synergy between the cellular automata and the powerful generalization capabilities of the neural networks. Evolutionary algorithms provide useful ways to learn about the structure of these neural networks, but the use of direct evolution in more difficult and complicated problems often fails to achieve satisfactory solutions. A more promising solution is to employ incremental evolution that reuses the solutions of easy tasks and applies these solutions to more difficult ones. Moreover, because the human brain can be divided into many behaviors with specific functionalities and because human beings can integrate these behaviors for high-level tasks, a biologically-inspired behavior selection mechanism is useful when combining these incrementally evolving basic behaviors. In this paper, an architecture based on cellular automata, neural networks, evolutionary algorithms, incremental evolution and a behavior selection mechanism is proposed to generate high-level behaviors for mobile robots. Experimental results with several simulations show the possibilities of the proposed architecture. Kyung-Joong Kim (Student Member, IEEE) received the B.S. and M.S. degree in computer science from Yonsei University, Seoul, Korea, in 2000 and 2002, respectively. Since 2002, he has been a Ph.D. student in the Department of Computer Science, Yonsei University. His research interests include evolutionary neural network, robot control, and agent architecture. Sung-Bae Cho (Member, IEEE) received the B.S. degree in computer science from Yonsei University, Seoul, Korea, in 1988 and the M.S. and Ph.D. degrees in computer science from Korea Advanced Institute of Science and Technology (KAIST), Taejeon, Korea, in 1990 and 1993, respectively. From 1991 to 1993, he worked as a Member of the Research Staff at the Center for Artificial Intelligence Research at KAIST. From 1993 to 1995, he was an Invited Researcher of Human Information Processing Research Laboratories at ATR (Advanced Telecommunications Research) Institute, Kyoto, Japan. In 1998, he was a Visiting Scholar at University of New South Wales, Canberra, Australia. Since 1995, he has been a Professor in the Department of Computer Science, Yonsei University. His research interests include neural networks, pattern recognition, intelligent man-machine interfaces, evolutionary computation, and artificial life. Dr. Cho is a Member of the Korea Information Science Society, INNS, the IEEE Computer Society, and the IEEE Systems, Man and Cybernetics Society. He was awarded outstanding paper prizes from the IEEE Korea Section in 1989 and 1992, and another one from the Korea Information Science Society in 1990. In 1993, he also received the Richard E. Merwin prize from the IEEE Computer Society. In 1994, he was listed in Who’s Who in Pattern Recognition from the International Association for Pattern Recognition and received the best paper awards at International Conference on Soft Computing in 1996 and 1998. In 1998, he received the best paper award at World Automation Congress. He was listed in Marquis Who’s Who in Science and Engineering in 2000 and in Marquis Who’s Who in the World in 2001.     

Comparative performance analysis of various artificial immune networks applied to RFID reader-to-reader collision avoidance

Within a radio frequency identification (RFID) system, the reader-to-reader collision problem may occur when a group of readers operate simultaneously. The scheduling-based family, as one branch of RIFD reader collision avoidance methods, focuses on the allocation of time slots and frequency channels to RFID readers. Generally, the RFID reader-to-reader collision avoidance model can be translated as an optimization problem related with the communication resource allocation by maximizing the total effective interrogation area. Artificial immune networks are emerging heuristic evolutionary algorithms, which have been broadly applied to scientific computing and engineering applications. Since the first version of artificial immune networks for optimization occurred, a series of revised or derived artificial immune networks have been developed which aim at capturing more accurate solutions at higher convergence speed. For the RFID reader-to-reader collision avoidance model, this paper attempts to investigate the performance of six artificial immune networks in allocating communication resources to multiple readers. By following the spirits of artificial immune networks, the corresponding major immune operators are redesigned to satisfy the practice of RFID systems. By taking into account the effects of time slots and frequency channels, respectively, two groups of simulation experiments are arranged to examine the effectiveness of different artificial immune networks in optimizing the total effective interrogation area. Besides, a group of examination is executed to investigate the performance of six algorithms in solving different dimensionality of solution space in reader collision avoidance model. Meanwhile, a single group of simulation experiments are arranged to examine the computational efficiency of six artificial immune networks. The results demonstrate that six artificial immune networks perform well in searching the maximum total effective interrogation and are suitable to solve the RFID reader-to-reader collision avoidance model.     

Evolving Genetic Networks for Synthetic Biology

The sibling disciplines, systems and synthetic biology, are engaged in unraveling the complexity of the biological networks. One is trying to understand the design principle of the existing networks while the other is trying to engineer artificial gene networks with predicted functions. The significant and important role that computational intelligence can play to steer the life engineering discipline towards its ultimate goal, has been acknowledged since its time of birth. However, as the field is facing many challenges in building complex modules/systems from the simpler parts/devices, whether from scratch or through redesign, the role of computational assistance becomes even more crucial. Evolutionary computation, falling under the broader domain of artificial intelligence, is well-acknowledged for its near optimal solution seeking capability for poorly known and partially understood problems. Since the post genome period, these natural-selection simulating algorithms are playing a noteworthy role in identifying, analyzing and optimizing different types of biological networks. This article calls attention to how evolutionary computation can help synthetic biologists in assembling larger network systems from the lego-like parts.     

DE-BP神经网络对产品成本预测的研究

引入BP神经网络算法对产品成本进行预测,建立了产品成本预测模型。针对神经网络参数优化容易陷入局部最优解的缺陷,结合差异演化算法,提出了DE-BP神经网络预测模型。实验表明,该算法具有较高的预测精度,能够为企业生产运营提供可靠的依据。