Biologically Inspired Computation
中科院计算机类SCI期刊及分区 月发布
ARTIFICIAL INTELLIGENCE IN MEDICINE COMPUTER VISION AND IMAGE UNDERSTANDING
Journal of Ambient Intelligence and Smart Environments NETWORK-COMPUTATION IN NEURAL SYSTEMS Information Technology and Control Intelligent Data Analysis AI MAGAZINE CONSTRAINTS
中科院计算机类SCI期刊及分区（20
期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS International Journal of Neural Systems
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION INTEGRATED COMPUTER-AIDED ENGINEERING IEEE Transactions on Cybernetics IEEE Transactions on Neural Networks and Learning Systems MEDICAL IMAGE ANALYSIS Information Fusion INTERNATIONAL JOURNAL OF COMPUTER VISION IEEE TRANSACTIONS ON IMAGE PROCESSING IEEE Computational Intelligence Magazine EVOLUTIONARY COMPUTATION IEEE INTELLIGENT SYSTEMS PATTERN RECOGNITION ARTIFICIAL INTELLIGENCE KNOWLEDGE-BASED SYSTEMS NEURAL NETWORKS EXPERT SYSTEMS WITH APPLICATIONS Swarm and Evolutionary Computation APPLIED SOFT COMPUTING DATA MINING AND KNOWLEDGE DISCOVERY INTERNATIONAL JOURNAL OF APPROXIMATE REASONING SIAM Journal on Imaging Sciences DECISION SUPPORT SYSTEMS Swarm Intelligence Fuzzy Optimization and Decision Making
General Terms
A Biologically Inspired Programming Modelfor Self-Healing SystemsSelvin George Department of Computer Science University of VirginiaCharlottesville, VAselvin@David EvansDepartment of Computer ScienceUniversity of VirginiaCharlottesville, VAevans@Lance DavidsonDepartment of BiologyUniversity of VirginiaCharlottesville, VAlance_davidson@ABSTRACTThere is an increasing need for software systems to be able to adapt to changing conditions of resource variability, component malfunction and malicious intrusion. Such self-healing systems can prove extremely useful in situations where continuous serviceis critical or manual repair is not feasible. Human efforts to engineer self-healing systems have had limited success, but nature has developed extraordinary mechanisms for robustness and self-healing over billions of years. Nature’s programs are encoded in DNA and exhibit remarkable density and expressiveness. We argue that the software engineering community can learn a great deal about building systems from the broader concepts surrounding biological cell programs and the strategies they use to robustly accomplish complex tasks such as development, healing and regeneration. We present a cell-based programming model inspired from biology and speculate on biologically inspired strategies for producing robust, scalable and self-healing software systems.Categories and Subject DescriptorsD.1.0 [Programming Techniques]: General; D.2.4 [Software/Program Verification] – reliability; F.1.1 [Models of Computation].General TermsDesign, Reliability, Experimentation, Security, Languages KeywordsBiological programming; self-healing systems; amorphous computing.1. INTRODUCTIONBiology is replete with examples of systems with remarkable robustness and self-healing properties. These include morphogenesis, wound healing and regeneration: Morphogenesis. A single cell develops into a full organism following a program encoded in its DNA that evolved over billions of years. Cells perform various actions like division, deformation and growth based on gene actions. The actions of the genes are dictated by the presence of chemical substances. Gene actions coupled with physical forces acting on a cell from its neighboring cells and external environment lead to a developed organism. Even in simple organisms, development is robust to many kinds of local failures and adapts to a wide range of environments. For example, when a cell dies, the neighboring cells sense changes in the environment and adapt their own development to correct the problem [6].Wound Healing. Almost all complex organisms have some sort of mechanism for healing simple wounds. In humans, when a minor injury happens, an inflammatory response occurs and the cells below the dermis (the deepest skin layer) begin to increase collagen (connective tissue) production. Later, the epithelial tissue (the outer skin layer) is regenerated. The interesting point here is the apparent level of awareness of the cells. Also, cells around the injury are able to adapt to a different function based on the new circumstances [2].Regeneration. Many organisms can regenerate new heads, limbs, internal organs or other body parts if the originals are lost or damaged. Organisms take two approaches to replacing a lost body part. Some, such as flatworms and the polyp Hydra, retain populations of stem cells throughout their lives, which are mobilized when needed. These stem cells retain the ability to regrow many of the body’s tissues. Other organisms, including newts, segmented worms and zebrafish, convert differentiated adult cells that have stopped dividing and form part of the skin, muscle or another tissue back into stem cells. When a newt’s leg, tail or eye is amputated or damaged, cells near the stump begin an extraordinary change. They revert from specialized skin, muscle and nerve cells into blank progenitor cells. These progenitors multiply quickly to about 80,000 cells and then grow into specialized cells to regenerate the missing part [5].We observe that nature’s approach to programming has the following properties:1. Environmental Awareness. Though the cells may havelimited communication capabilities, they act differently in response to sensed properties of the surrounding environment. This enables cells to react to changes in nearbycells, as well as the surrounding environment.Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.WOSS '02, Nov 18-19, 2002, Charleston, SC, USA.Copyright 2002 ACM 1-58113-609-9/02/0011 ...$5.002. Adaptation. Many cells have a great amount of adaptability.In many organisms, at the beginning of morphogenesis, if one of the initial cells obtained by the first division of the germ cell dies then the surviving cell is often able to complete the development of the organism. This indicates that enough information is preserved to be able to “backtrack” to a previous state of development. This is due to the fact that all cells run the same cell-program and can hence respond to aberrant behavior from neighbors3. Redundancy. A non-redundant organism would have everycell assigned a fixed role in the development process. Any failure during the development process would produce a defective organism. Typical organisms have many cells devoted to the same function throughout development, so that failures of individual cells are inconsequential.Biological systems also exhibit redundancy of function, where several distinct mechanisms evolve for the same purpose in a single organism.4. Decentralization. There is no global coordination andlimited communication for most of the development process.Cells sense properties of their environment, and are most affected by nearby cells. Cells can induce neighboring cells to do a particular action, but there is no centralized control and limited long-distance communication.2. CELL-BASED PROGRAMMINGInspired by biological systems, we propose a cell-based programming model that can be used for software systems operation and healing. Our model is similar to the cellular automata that have been studied extensively since von Neumann’s early work [4], but differs in that it is more closely related to biological processes. In particular, we support a notion of cell division, a communication model based on chemical diffusion, and a rudimentary model of the physical forces involved. By developing a programming model more like nature’s, we believe we will produce more robust programs with natural self-healing properties.A related approach is amorphous computing, which considers approaches for programming a medium of randomly distributed computing particles. The Growing Point Language [1] and Origami Shape Language [3] both illustrate mechanisms for global self-organization using simple local communication of the agents. Self-healing properties are also being studied using GPL. As with our work, the challenge is to produce programs that generate predictable behavior with a locally unpredictable and non-traditional programming model. Because the underlying execution environment is inherently redundant and decentralized, robustness is practically inevitable if programs are constructed in the right way.We represent a cell program as an automaton containing discrete states and transitions between these states. Every cell comprising the program is in one of these states. The input to each cell state is the sensed properties of the local environment and the output is a transition to another state, or a division into two (possibly different) states. States are represented by circles and state transitions by directional arrows. Dots represent cell divisions.Our cell programming model incorporates: 1. Cell Division. A cell can divide into two daughter cells thatmay be dissimilar in orientation and chemical composition but have the same program (DNA). A cell has an axis calledthe apical-basal axis. Divisions can be either perpendicular tothis cell axis or along the plane containing the axis. The difference in chemical composition and also the different chemicals on their cell walls causes the two daughter cells tobehave differently from that point onwards. Cell division ismodeled by using a transition from one state to two states.2. Cell Actions. Cells can produce proteins and signalingchemicals depending on what genes are active. Chemicals produced this way affect the environment and neighboring cells through chemical diffusion.3. Gene Actions. Genes can activate or deactivate dependingon the presence or absence of a particular protein or a certaindegree of chemical concentration. Activation or deactivationof a gene results in cell actions like production of chemicals. The varying degrees of concentration produced by earlier cell actions (both by the cell and its neighbors) cause gene actions and gene actions cause cell actions; this results in a powerful programming paradigm. Cell actions such as production of chemicals are modeled using messages. Gene actions are modeled using cell state transitions; these are a result of received messages.A cell program begins with cells in an initial configuration, and all the cells follow transition rules like a finite state machine. Between steps, an environment simulator determines changes in external stimuli. The changes to the environment can be due to operations of the software system, expected input conditions or failure conditions. Since cells can sense their local environment it is possible for them to be able to perform failure recovery (healing) or re-composition of appropriate components (regeneration). Our simulator also provides opportunities to conduct experiments involving random and catastrophic failures. Two simple examples of cell programs are shown in Figure 1. Automaton A produces a line of cells as long as the input condition a exists. The condition a may represent the presence of food for growth. AutomatonB produces cells to combat intruders as long as it detects unfavorable conditions. This approach creates excess cells so that some may survive the malicious action.Figure 1. Example Cell ProgramsA. Creating a line of Cellsa – Condition favorableto continued cellproductionB. Robustness through replicationa – Favorable conditionsb – Unfavorable conditionsNumbered Circle – StateArrow – TransitionDot – Cell division3. SIMULATING CELL PROGRAMSUsing a simulator, we have conducted simulations of different cell programs. The simulator simulates a cell program on a simulation configuration. The simulation configuration is used to introduce new cells, chemical concentrations or failures. Our simulator is available at /cellsim .A sample program for creation of a self-healing blastula is shown below. A blastula is a spherical structure that is the first stage of development of many large organisms. A sufficient number of cells are needed before organism development proceeds to the next stage.state s1 {emits (sig, 0.1)transitions(0 <= sig <= 0.375) -> (s2, s2) axis; -> (s1); }state s2 {emits (sig, 0.1) transitions(0 <= sig <= 0.375) -> (s3, s3) normal-X; -> (s2); }state s3 {emits (sig, 0.1) transitions(0 <= sig <= 0.375) -> (s1, s1) normal-Y; -> (s3); }In the above cell automaton there are three cell-states – s1, s2 and s3. They are similar in that they emit the same signaling chemical sig and divide into two cells each if they sense that the concentration of sig is less than 0.375. The cell remains in its current state if the concentration of sig is above 0.375.(a)(b)(c)Figure 2. Simulated Blastula Program. (a) Blastula in 8-cell stage – starting from one cell; (b) damaged blastula – after killing one cell (c) after the blastula regenerates.This self-healing blastula has the property that if a few cells are killed, it will automatically heal itself by producing the required number of cells. Figure 2 shows a simulation of the blastula program for four steps, after which one of the cells was killed to observe the self-healing behavior. The surviving cells regenerate additional cells to continue the process. The principle behind this type of healing is that the once a cell was killed, it stopped producing the particular chemical that was being sensed by its neighbors. Note that nothing in the cell program explicitly deals with healing and regeneration. The neighbors of a failed cell just follow a different path in the cell-program due to the changed environmental conditions.4. TOWARDS SELF-HEALING SYSTEMSAlthough our initial experiments have focused on mimicking simple biological processes and generating basic geometric structures, our long-term goal is to develop techniques that can be used to produce robust, self-healing systems designed to perform a complex task. Developing complex programs using state diagrams, however, is infeasible. A high-level programming abstraction for cell-based programs is needed in which a programmer can describe desired processes at a high-level and a cell-program compiler will produce the steps of the automaton. An important design issue is which operations or abstractions should be part of the language and which can be composed of the elementary operations and hence can be kept outside the language. We are currently working on programming abstractions based on the biological cell model. If successful, programs described in this way will have intrinsic robustness, scaling and self-healing properties. We hope that our experiments with biological programs will provide insights into how to build more robust computer systems.5. ACKNOWLEDGMENTSThis work was funded in part by grants from the National Science Foundation (CCR-0092945 and EIA-0205327) and NASA Langley Research Center.6. REFERENCES[1] H. Abelson, D. Allen, D. Coore, C. Hanson, G. Homsy, T.Knight, R. Nagpal, E. Rauch, G. Sussman and R. Weiss, Amorphous Computing, Communications of the ACM, Volume 43, Number 5, p. 74-83. May 2000.[2] Mary Y. Mazzotta. Nutrition and wound healing . Journal ofthe American Podiatric Medical Association. Volume 84, Number 9, p. 456–62. September 1994. [3] Radhika Nagpal, Programmable Self-Assembly:Constructing Global Shape using Biologically-inspired Local Interactions and Origami Mathematics, PhD Thesis, MIT Department of Electrical Engineering and Computer Science, June 2001. [4] John von Neumann, Theory of Self-Reproducing Automata.University of Illinois Press, 1966 (Originally published in 1953).[5] Helen Pearson, The regeneration gap , Nature ScienceUpdate. 22 November 2001. [6] Lewis Wolpert, Rosa Beddington, Peter Lawrence, ThomasM. Jessell, Principles of Development , Oxford University Press. 2002.。
biocomputer
The first case of Biocompers
被引用2749次
To solve the Hamiltonian path problem
汉弥尔顿路径问题（Hamiltonian path problem）属于数 学中的图论。此问题是用来决定一个图上的汉弥尔顿 路径。为NP完全问题。为旅行推销员问题的特殊案例。 问题的具体描述是指，给定一个城市的集合和它们之 间的道路，找到一个旅行指南集合，在一个给定的城 市开始，到一个给定的城市结束，而且途径且仅途径 一次其他的城市。
一个有限状态的决策机
生物计算机中的逻辑运算
The internal structure of an RNAibased biocomputer
生物计算机中的逻辑运算
The computer logic relations between biological inputs
Some cases of Biocomputers
DNA 计算机历史 (2)
2001年11月，以色列科学家成功研制成世界第一台DNA计算机，它的输出、输入和软 硬件全由在活性有机体中储存和处理编码信息的DNA分子组成。该计算机不过一滴水 大小，比较原始，也没有任何相关应用产生，但这是未来DNA计算机的雏形。次年， 研究人员又作了改进，吉尼斯世界记录称之为“最小的生物计算设备”。 2002年2月，DNA计算机的研究则更进一步，日本奥林巴斯（Olympus） 公司宣布，该 公司与东京大学联合开发出了全球第一台能够真正投入商业应用的DNA计算机。他们 开发的这种DNA计算机有分子计算组件和电子计算机部件两部分组成。前者用来计算 分子的DNA组合，以实现生化反应，搜索并筛选出正确的DNA结果，后者则可以对这些 结果进行分析。 DNA计算机已经成为当前世界许多国家科研人员研究的热点之一，而且取得了突破性 进展，但主要还处在理论研究和应用探索阶段。
DNA计算模型原理讲解学习资料
DNA蛋白质计算的优点
将其转化为蛋白质序列的优点在于三个碱 基序列对应一种蛋白质，因此蛋白质序列 其信息密度更大，分子更小，从而可以使 用质谱分析法对蛋白质序列进行检测。
参考文献
[1]张成, 杨静, 王淑栋. DNA计算中荧光技术的应用及其发展[J].计算机学报, 2009, 32(12):2300-2310.
其与传统的进化算法相比，一方面DNA进化算法 的可以实现更大的群体规模；另一方面随着群体 规模的增大，DNA计算的并行性不会导致搜索时 间的增加。
DNA蛋白质算法
这种方法使用质粒进行运算，将DNA计算与其相应的蛋白 质表达相结合。研究表明，通过在质粒上设置开放读取框 架(ORF)，我们正在将质粒计算模型与蛋白质表达相结合 进行运算。 开放读取框架如图所示：
[2]孙伟, 尤加宇, 江宏, 等. 纳米粒子标记 DNA 探针的制备与检测应用[J]. 中国卫生检验杂 志, 2005, 15( 8): 1008.
[3] Rohani Binti Abu Bakar,Junzo Watada, Witold Pedrycz. DNA approach to solve clustering problem based on a mutual order[J]. BioSystems. 2008, 91: 1-12.
文献[8]使用左图发夹模型解决了3-SAT 问题
帽子（cap）结构的发夹，文献[9]使 用右图这种结构作为辅助结构解决了 3-SAT问题
链状结构
另外一种线性存储模型是使用单链DNA或者RNA链来编码 信息，然后通过分管-合管操作来实现对问题的求解。特 别指出的是文献[10]中使用RNA作为计算底物，其删除操 作使用的是核糖核酸酶H，因为该酶的特性就是可以切割 RNA的端，进而消化DNA/RNA双螺旋链，而单链结构不 被破坏。
计算机电子通信等信息类SCI期刊大全
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COLLOQUIUM375 263 CMC-Computers Materials & Continua315 264 ACM SIGPLAN NOTICES2541 265 Advances in Difference Equations849266 Iranian Journal of Science and Technology-Transactions of ElectricalEngineering22267 Rhetoric Society Quarterly204 268 Glasnik Matematicki256 269 NARRATIVE INQUIRY379 270 Mathematical Communications204 271 ARCHIVE FOR HISTORY OF EXACT SCIENCES285 272 JOURNAL OF APPLIED COMMUNICATION RESEARCH692 273 Bollettino di Storia delle Scienze Matematiche15 274 Economic Computation and Economic Cybernetics Studies and Research103 275 INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING345 276 DYNAMIC SYSTEMS AND APPLICATIONS264 277 Mathematical Population Studies170278 University Politehnica of Bucharest Scientific Bulletin-Series A-Applied Mathematics and Physics184279 IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES1381280 UTILITAS MATHEMATICA482 281 HISTORIA MATHEMATICA221 282 MICROWAVE JOURNAL396 283 CRYPTOLOGIA160 284 Applied Mathematics-A Journal of Chinese Universities Series B174285 Acta Mathematicae Applicatae Sinica-English Series538 286 PROGRAMMING AND COMPUTER SOFTWARE114 287 Ukrainian Mathematical Journal699 288 International Journal of Transport Economics189 289 JOURNAL OF MEDIA ECONOMICS187 290 Electronics and Communications in Japan167 291 FUJITSU SCIENTIFIC & TECHNICAL JOURNAL173 292 INFOR380 293 ELECTRICAL ENGINEERING IN JAPAN398 294 African Journalism Studies12 295 Tijdschrift voor Communicatiewetenschap42 296 Journal of African Media Studies66 297 ICGA JOURNAL63 298 Pure and Applied Mathematics Quarterly241 299 Light & Engineering41 300 EPE Journal101 301 SOLID STATE TECHNOLOGY162 302 Journal of the Institute of Telecommunications Professionals7 303 Traitement du Signal72 304 ELECTRONICS WORLD45 305 Road & Transport Research115 306 IEEE Transactions on Cognitive and Developmental Systems7影响因子eigenFac torScore1.1470.00259 1.130.00268 1.1180.00026 1.1160.00199 1.0790.00047 1.0750.00154 1.0750.00256 1.0670.00255 1.0660.00263 1.060.00221 1.0560.00071 1.0390.00113 1.0370.00082 1.0340.00068 1.0320.00826 1.0310.00048 1.0290.00039 1.0250.0011 1.010.0019310.0010210.0003710.000310.0007710.00015 0.9940.00151 0.9760.00109 0.9640.00094 0.9560.00213 0.9510.00704 0.950.00089 0.9450.00191 0.9430.00039 0.920.00244 0.9090.00033 0.9090.00028 0.9030.00178 0.9020.000520.90.00064 0.8970.00021 0.8930.00155 0.8850.00058 0.8780.00115 0.8770.0015 0.8750.00035 0.8750.001410.8670.00027 0.8630.00089 0.8590.00126 0.850.00116 0.850.00275 0.8490.00036 0.8380.00073 0.8370.00093 0.8270.00279 0.8270.00196 0.8130.00085 0.8120.00113 0.8110.00071 0.8090.00033 0.8070.001130.80.00168 0.7910.00103 0.7890.00048 0.7810.00166 0.7780.00063 0.7780.00084 0.7750.00031 0.7720.00113 0.7720.00063 0.7710.00099 0.770.00081 0.7690.00031 0.7690.00137 0.7660.00162 0.7650.00137 0.7595.00E-05 0.7580.00018 0.7570.00654 0.7540.00261 0.7530.00029 0.750.00054 0.7480.00625 0.7460.00106 0.740.00067 0.7380.00104 0.7370.00029 0.7350.00516 0.7330.0004 0.7320.00052 0.7310.00754 0.730.00145 0.7290.00109 0.7270.00024 0.7270.00019 0.7240.006450.7240.0003 0.7230.001760.720.00137 0.7130.00235 0.7110.00301 0.7110.00349 0.710.000630.7060.00048 0.7050.00064 0.6980.01629 0.6960.00112 0.6960.00024 0.6940.00057 0.6920.00017 0.6880.00125 0.6870.0028 0.6860.00028 0.6770.00081 0.6750.00073 0.6740.00046 0.670.0028 0.6670.00099 0.6670.00024 0.6610.00337 0.6570.0003 0.6540.00098 0.6520.00049 0.6470.0037 0.6470.00055 0.6460.00131 0.6450.0029 0.6440.00041 0.6430.00078 0.6430.00081 0.6420.00151 0.6410.00128 0.6380.00057 0.6350.00038 0.6320.00082 0.6310.00102 0.6280.00031 0.6270.00047 0.6250.00235 0.6230.00216 0.6220.00016 0.6220.00065 0.6220.001030.6190.00061 0.6180.00058 0.6090.00048 0.6090.00097 0.6050.002090.60.000420.68.00E-05 0.5970.00124 0.5970.00354 0.5970.00047 0.5950.00038 0.5810.00081 0.580.00019 0.5790.00023 0.5780.00107 0.5750.00025 0.5750.0015 0.5730.00199 0.5690.00028 0.5670.0006 0.5650.00321 0.5620 0.5620.00043 0.5620.00024 0.560.00063 0.5580.00067 0.5560.00106 0.5560.00037 0.5480.00163 0.5480.00142 0.5480.00036 0.5440.0022 0.5430.01147 0.5430.00127 0.5420.00125 0.5290.00056 0.5290.00152 0.5250.00183 0.5170.00076 0.5160.00035 0.5160.00058 0.5150.00049 0.5150.00073 0.5140.00045 0.5130.00075 0.5130.00287 0.5090.00091 0.5030.001560.50.000190.50.00020.50.00028 0.4970.00346 0.4970.00073 0.4890.00428 0.4880.000530.4870.00121 0.4860.00149 0.4860.00078 0.4840.00051 0.4840.00072 0.4830.00043 0.4810.000750.4780.00016 0.4770.00098 0.4750.00031 0.4690.00161 0.4680.00091 0.4680.00089 0.4650.00034 0.4640.00046 0.4630.00053 0.4620.00025 0.4620.00044 0.4628.00E-05 0.460.00027 0.4570.00109 0.4570.0012 0.4560.00189 0.4550.00111 0.4520.00119 0.4510.00068 0.450.00144 0.4480.00071 0.4480.00083 0.4460.0042 0.4460.00076 0.4440.00099 0.4440.00201 0.4410.00124 0.4410.00194 0.4390.00074 0.4360.00318 0.4350.000110.4310.00017 0.4260.000370.4260.0006 0.4240.00099 0.4230.00131 0.4220.00052 0.4190.00037 0.4190.00145 0.4150.00129 0.4150.00079 0.4130.00033 0.4110.00196 0.4050.00018 0.4050.00163 0.3940.00125 0.3940.00023 0.390.00185 0.380.00041 0.3790.00125 0.3770.00551 0.3750.0001 0.3650.00087 0.3650.0002 0.3570.00117 0.3480.00015 0.3460.00068 0.3430.00142 0.3390.00093 0.3350.0032 0.3350.002330.3335.00E-05 0.3330.0003 0.3280.00049 0.3170.00043 0.3160.00059 0.3080.00042 0.3080.000720.30 0.2990.00019 0.2990.00054 0.2980.00052 0.2860.000210.2790.000340.2740.00196 0.2610.00082 0.2580.00028 0.2580.00063 0.2563.00E-05 0.2470.000490.2420.001060.230.000170.2280.000790.220.000210.2170.000170.2020.000360.1910.000220.1890.000250.1880.000330.1712.00E-050.1717.00E-050.1540.000180.1520.000280.1490.001410.1183.00E-050.0910.000110.0820.00010.07800.0281.00E-050.0262.00E-050.0210.00014 -999.9991.00E-05。
外文文献—遗传算法
附录I 英文翻译第一部分英文原文文章来源：书名：《自然赋予灵感的元启发示算法》第二、三章出版社：英国Luniver出版社出版日期：2008Chapter 2Genetic Algorithms2.1 IntroductionThe genetic algorithm (GA), developed by John Holland and his collaborators in the 1960s and 1970s, is a model or abstraction of biolo gical evolution based on Charles Darwin’s theory of natural selection. Holland was the first to use the crossover and recombination, mutation, and selection in the study of adaptive and artificial systems. These genetic operators form the essential part of the genetic algorithm as a problem-solving strategy. Since then, many variants of genetic algorithms have been developed and applied to a wide range of optimization problems, from graph colouring to pattern recognition, from discrete systems (such as the travelling salesman problem) to continuous systems (e.g., the efficient design of airfoil in aerospace engineering), and from financial market to multiobjective engineering optimization.There are many advantages of genetic algorithms over traditional optimization algorithms, and two most noticeable advantages are: the ability of dealing with complex problems and parallelism. Genetic algorithms can deal with various types of optimization whether the objective (fitness) functionis stationary or non-stationary (change with time), linear or nonlinear, continuous or discontinuous, or with random noise. As multiple offsprings in a population act like independent agents, the population (or any subgroup) can explore the search space in many directions simultaneously. This feature makes it ideal to parallelize the algorithms for implementation. Different parameters and even different groups of strings can be manipulated at the same time.However, genetic algorithms also have some disadvantages.The formulation of fitness function, the usage of population size, the choice of the important parameters such as the rate of mutation and crossover, and the selection criteria criterion of new population should be carefully carried out. Any inappropriate choice will make it difficult for the algorithm to converge, or it simply produces meaningless results.2.2 Genetic Algorithms2.2.1 Basic ProcedureThe essence of genetic algorithms involves the encoding of an optimization function as arrays of bits or character strings to represent the chromosomes, the manipulation operations of strings by genetic operators, and the selection according to their fitness in the aim to find a solution to the problem concerned. This is often done by the following procedure:1) encoding of the objectives or optimization functions; 2) defining a fitness function or selection criterion; 3) creating a population of individuals; 4) evolution cycle or iterations by evaluating the fitness of allthe individuals in the population,creating a new population by performing crossover, and mutation,fitness-proportionate reproduction etc, and replacing the old population and iterating again using the new population;5) decoding the results to obtain the solution to the problem. These steps can schematically be represented as the pseudo code of genetic algorithms shown in Fig. 2.1.One iteration of creating a new population is called a generation. The fixed-length character strings are used in most of genetic algorithms during each generation although there is substantial research on the variable-length strings and coding structures.The coding of the objective function is usually in the form of binary arrays or real-valued arrays in the adaptive genetic algorithms. For simplicity, we use binary strings for encoding and decoding. The genetic operators include crossover,mutation, and selection from the population.The crossover of two parent strings is the main operator with a higher probability and is carried out by swapping one segment of one chromosome with the corresponding segment on another chromosome at a random position (see Fig.2.2).The crossover carried out in this way is a single-point crossover. Crossover at multiple points is also used in many genetic algorithms to increase the efficiency of the algorithms.The mutation operation is achieved by flopping the randomly selected bits (see Fig. 2.3), and the mutation probability is usually small. The selection of anindividual in a population is carried out by the evaluation of its fitness, and it can remain in the new generation if a certain threshold of the fitness is reached or the reproduction of a population is fitness-proportionate. That is to say, the individuals with higher fitness are more likely to reproduce.2.2.2 Choice of ParametersAn important issue is the formulation or choice of an appropriate fitness function that determines the selection criterion in a particular problem. For the minimization of a function using genetic algorithms, one simple way of constructing a fitness function is to use the simplest form F = A−y with A being a large constant (though A = 0 will do) and y = f(x), thus the objective is to maximize the fitness function and subsequently minimize the objective function f(x). However, there are many different ways of defining a fitness function.For example, we can use the individual fitness assignment relative to the whole populationwhere is the phenotypic value of individual i, and N is the population size. The appropriateform of the fitness function will make sure that the solutions with higher fitness should be selected efficiently. Poor fitness function may result in incorrect or meaningless solutions.Another important issue is the choice of various parameters.The crossover probability is usually very high, typically in the range of 0.7~1.0. On the other hand, the mutation probability is usually small (usually 0.001 _ 0.05). If is too small, then the crossover occurs sparsely, which is not efficient for evolution. If the mutation probability is too high, the solutions could still ‘jump around’ even if the optimal solution is approaching.The selection criterion is also important. How to select the current population so that the best individuals with higher fitness should be preserved and passed onto the next generation. That is often carried out in association with certain elitism. The basic elitism is to select the most fit individual (in each generation) which will be carried over to the new generation without being modified by genetic operators. This ensures that the best solution is achieved more quickly.Other issues include the multiple sites for mutation and the population size. The mutation at a single site is not very efficient, mutation at multiple sites will increase the evolution efficiency. However, too many mutants will make it difficult for the system to converge or even make the system go astray to the wrong solutions. In reality, if the mutation is too high under high selection pressure, then the whole population might go extinct.In addition, the choice of the right population size is also very important. If the population size is too small, there is not enough evolution going on, and there is a risk for the whole population to go extinct. In the real world, a species with a small population, ecological theory suggests that there is a real danger of extinction for such species. Even the system carries on, there is still a danger of premature convergence. In a small population, if a significantly more fit individual appears too early, it may reproduces enough offsprings so that they overwhelm the whole (small) population. This will eventually drive the system to a local optimum (not the global optimum). On the other hand, if the population is too large, more evaluations of the objectivefunction are needed, which will require extensive computing time.Furthermore, more complex and adaptive genetic algorithms are under active research and the literature is vast about these topics.2.3 ImplementationUsing the basic procedure described in the above section, we can implement the genetic algorithms in any programming language. For simplicity of demonstrating how it works, we have implemented a function optimization using a simple GA in both Matlab and Octave.For the generalized De Jong’s test function where is a positive integer andr > 0 is the half length of the domain. This function has a minimum of at . For the values of , r = 100 and n = 5 as well as a population size of 40 16-bit strings, the variations of the objective function during a typical run are shown in Fig. 2.4. Any two runs will give slightly different results dueto the stochastic nature of genetic algorithms, but better estimates are obtained as the number of generations increases.For the well-known Easom functionit has a global maximum at (see Fig. 2.5). Now we can use the following Matlab/Octave to find its global maximum. In our implementation, we have used fixedlength 16-bit strings. The probabilities of crossover and mutation are respectivelyAs it is a maximization problem, we can use the simplest fitness function F = f(x).The outputs from a typical run are shown in Fig. 2.6 where the top figure shows the variations of the best estimates as they approach while the lower figure shows the variations of the fitness function.% Genetic Algorithm (Simple Demo) Matlab/Octave Program% Written by X S Yang (Cambridge University)% Usage: gasimple or gasimple(‘x*exp(-x)’);function [bestsol, bestfun,count]=gasimple(funstr)global solnew sol pop popnew fitness fitold f range;if nargin<1,% Easom Function with fmax=1 at x=pifunstr=‘-cos(x)*exp(-(x-3.1415926)^2)’;endrange=[-10 10]; % Range/Domain% Converting to an inline functionf=vectorize(inline(funstr));% Generating the initil populationrand(‘state’,0’); % Reset the random generatorpopsize=20; % Population sizeMaxGen=100; % Max number of generationscount=0; % counternsite=2; % number of mutation sitespc=0.95; % Crossover probabilitypm=0.05; % Mutation probabilitynsbit=16; % String length (bits)% Generating initial populationpopnew=init_gen(popsize,nsbit);fitness=zeros(1,popsize); % fitness array% Display the shape of the functionx=range(1):0.1:range(2); plot(x,f(x));% Initialize solution <- initial populationfor i=1:popsize,solnew(i)=bintodec(popnew(i,:));end% Start the evolution loopfor i=1:MaxGen,% Record as the historyfitold=fitness; pop=popnew; sol=solnew;for j=1:popsize,% Crossover pairii=floor(popsize*rand)+1; jj=floor(popsize*rand)+1;% Cross overif pc>rand,[popnew(ii,:),popnew(jj,:)]=...crossover(pop(ii,:),pop(jj,:));% Evaluate the new pairscount=count+2;evolve(ii); evolve(jj);end% Mutation at n sitesif pm>rand,kk=floor(popsize*rand)+1; count=count+1;popnew(kk,:)=mutate(pop(kk,:),nsite);evolve(kk);endend % end for j% Record the current bestbestfun(i)=max(fitness);bestsol(i)=mean(sol(bestfun(i)==fitness));end% Display resultssubplot(2,1,1); plot(bestsol); title(‘Best estimates’); subplot(2,1,2); plot(bestfun); title(‘Fitness’);% ------------- All sub functions ----------% generation of initial populationfunction pop=init_gen(np,nsbit)% String length=nsbit+1 with pop(:,1) for the Signpop=rand(np,nsbit+1)>0.5;% Evolving the new generationfunction evolve(j)global solnew popnew fitness fitold pop sol f;solnew(j)=bintodec(popnew(j,:));fitness(j)=f(solnew(j));if fitness(j)>fitold(j),pop(j,:)=popnew(j,:);sol(j)=solnew(j);end% Convert a binary string into a decimal numberfunction [dec]=bintodec(bin)global range;% Length of the string without signnn=length(bin)-1;num=bin(2:end); % get the binary% Sign=+1 if bin(1)=0; Sign=-1 if bin(1)=1.Sign=1-2*bin(1);dec=0;% floating point.decimal place in the binarydp=floor(log2(max(abs(range))));for i=1:nn,dec=dec+num(i)*2^(dp-i);enddec=dec*Sign;% Crossover operatorfunction [c,d]=crossover(a,b)nn=length(a)-1;% generating random crossover pointcpoint=floor(nn*rand)+1;c=[a(1:cpoint) b(cpoint+1:end)];d=[b(1:cpoint) a(cpoint+1:end)];% Mutatation operatorfunction anew=mutate(a,nsite)nn=length(a); anew=a;for i=1:nsite,j=floor(rand*nn)+1;anew(j)=mod(a(j)+1,2);endThe above Matlab program can easily be extended to higher dimensions. In fact, there is no need to do any programming (if you prefer) because there are many software packages (either freeware or commercial) about genetic algorithms. For example, Matlab itself has an extra optimization toolbox.Biology-inspired algorithms have many advantages over traditional optimization methods such as the steepest descent and hill-climbing and calculus-based techniques due to the parallelism and the ability of locating the very good approximate solutions in extremely very large search spaces.Furthermore, more powerful new generation algorithms can be formulated by combiningexisting and new evolutionary algorithms with classical optimization methods.Chapter 3Ant AlgorithmsFrom the discussion of genetic algorithms, we know that we can improve the search efficiency by using randomness which will also increase the diversity of the solutions so as to avoid being trapped in local optima. The selection of the best individuals is also equivalent to use memory. In fact, there are other forms of selection such as using chemical messenger (pheromone) which is commonly used by ants, honey bees, and many other insects. In this chapter, we will discuss the nature-inspired ant colony optimization (ACO), which is a metaheuristic method.3.1 Behaviour of AntsAnts are social insects in habit and they live together in organized colonies whose population size can range from about 2 to 25 millions. When foraging, a swarm of ants or mobile agents interact or communicate in their local environment. Each ant can lay scent chemicals or pheromone so as to communicate with others, and each ant is also able to follow the route marked with pheromone laid by other ants. When ants find a food source, they will mark it with pheromone and also mark the trails to and from it. From the initial random foraging route, the pheromone concentration varies and the ants follow the route with higher pheromone concentration, and the pheromone is enhanced by the increasing number of ants. As more and more ants follow the same route, it becomes the favoured path. Thus, some favourite routes (often the shortest or more efficient) emerge. This is actually a positive feedback mechanism.Emerging behaviour exists in an ant colony and such emergence arises from simple interactions among individual ants. Individual ants act according to simple and local information (such as pheromone concentration) to carry out their activities. Although there is no master ant overseeing the entire colony and broadcasting instructions to the individual ants, organized behaviour still emerges automatically. Therefore, such emergent behaviour is similar to other self-organized phenomena which occur in many processes in nature such as the pattern formation in animal skins (tiger and zebra skins).The foraging pattern of some ant species (such as the army ants) can show extraordinary regularity. Army ants search for food along some regular routes with an angle of about apart. We do not know how they manage to follow such regularity, but studies show that they could move in an area and build a bivouac and start foraging. On the first day, they forage in a random direction, say, the north and travel a few hundred meters, then branch to cover a large area. The next day, they will choose a different direction, which is about from the direction on the previous day and cover a large area. On the following day, they again choose a different direction about from the second day’s direction. In this way, they cover the whole area over about 2 weeks and they move out to a different location to build a bivouac and forage again.The interesting thing is that they do not use the angle of (this would mean that on the fourth day, they will search on the empty area already foraged on the first day). The beauty of this angle is that it leaves an angle of about from the direction on the first day. This means they cover the whole circle in 14 days without repeating (or covering a previously-foraged area). This is an amazing phenomenon.3.2 Ant Colony OptimizationBased on these characteristics of ant behaviour, scientists have developed a number ofpowerful ant colony algorithms with important progress made in recent years. Marco Dorigo pioneered the research in this area in 1992. In fact, we only use some of the nature or the behaviour of ants and add some new characteristics, we can devise a class of new algorithms.The basic steps of the ant colony optimization (ACO) can be summarized as the pseudo code shown in Fig. 3.1.Two important issues here are: the probability of choosing a route, and the evaporation rate of pheromone. There are a few ways of solving these problems although it is still an area of active research. Here we introduce the current best method. For a network routing problem, the probability of ants at a particular node to choose the route from node to node is given bywhere and are the influence parameters, and their typical values are .is the pheromone concentration on the route between and , and the desirability ofthe same route. Some knowledge about the route such as the distance is often used so that ,which implies that shorter routes will be selected due to their shorter travelling time, and thus the pheromone concentrations on these routes are higher.This probability formula reflects the fact that ants would normally follow the paths with higher pheromone concentrations. In the simpler case when , the probability of choosing a path by ants is proportional to the pheromone concentration on the path. The denominator normalizes the probability so that it is in the range between 0 and 1.The pheromone concentration can change with time due to the evaporation of pheromone. Furthermore, the advantage of pheromone evaporation is that the system could avoid being trapped in local optima. If there is no evaporation, then the path randomly chosen by the first ants will become the preferred path as the attraction of other ants by their pheromone. For a constant rate of pheromone decay or evaporation, the pheromone concentration usually varies with time exponentiallywhere is the initial concentration of pheromone and t is time. If , then we have . For the unitary time increment , the evaporation can beapproximated by . Therefore, we have the simplified pheromone update formula:where is the rate of pheromone evaporation. The increment is the amount of pheromone deposited at time t along route to when an ant travels a distance . Usually . If there are no ants on a route, then the pheromone deposit is zero.There are other variations to these basic procedures. A possible acceleration scheme is to use some bounds of the pheromone concentration and only the ants with the current global best solution(s) are allowed to deposit pheromone. In addition, certain ranking of solution fitness can also be used. These are hot topics of current research.3.3 Double Bridge ProblemA standard test problem for ant colony optimization is the simplest double bridge problem with two branches (see Fig. 3.2) where route (2) is shorter than route (1). The angles of these two routes are equal at both point A and pointB so that the ants have equal chance (or 50-50 probability) of choosing each route randomly at the initial stage at point A.Initially, fifty percent of the ants would go along the longer route (1) and the pheromone evaporates at a constant rate, but the pheromone concentration will become smaller as route (1) is longer and thus takes more time to travel through. Conversely, the pheromone concentration on the shorter route will increase steadily. After some iterations, almost all the ants will move along the shorter route. Figure 3.3 shows the initial snapshot of 10 ants (5 on each route initially) and the snapshot after 5 iterations (or equivalent to 50 ants have moved along this section). Well, there are 11 ants, and one has not decided which route to follow as it just comes near to the entrance.Almost all the ants (well, about 90% in this case) move along the shorter route.Here we only use two routes at the node, it is straightforward to extend it to the multiple routes at a node. It is expected that only the shortest route will be chosen ultimately. As any complex network system is always made of individual nodes, this algorithms can be extended to solve complex routing problems reasonably efficiently. In fact, the ant colony algorithms have been successfully applied to the Internet routing problem, the travelling salesman problem, combinatorial optimization problems, and other NP-hard problems.3.4 Virtual Ant AlgorithmAs we know that ant colony optimization has successfully solved NP-hard problems such asthe travelling salesman problem, it can also be extended to solve the standard optimization problems of multimodal functions. The only problem now is to figure out how the ants will move on an n-dimensional hyper-surface. For simplicity, we will discuss the 2-D case which can easily be extended to higher dimensions. On a 2D landscape, ants can move in any direction or , but this will cause some problems. How to update the pheromone at a particular point as there are infinite number of points. One solution is to track the history of each ant moves and record the locations consecutively, and the other approach is to use a moving neighbourhood or window. The ants ‘smell’ the pheromone concentration of their neighbourhood at any particular location.In addition, we can limit the number of directions the ants can move by quantizing the directions. For example, ants are only allowed to move left and right, and up and down (only 4 directions). We will use this quantized approach here, which will make the implementation much simpler. Furthermore, the objective function or landscape can be encoded into virtual food so that ants will move to the best locations where the best food sources are. This will make the search process even more simpler. This simplified algorithm is called Virtual Ant Algorithm (VAA) developed by Xin-She Yang and his colleagues in 2006, which has been successfully applied to topological optimization problems in engineering.The following Keane function with multiple peaks is a standard test functionThis function without any constraint is symmetric and has two highest peaks at (0, 1.39325) and (1.39325, 0). To make the problem harder, it is usually optimized under two constraints:This makes the optimization difficult because it is now nearly symmetric about x = y and the peaks occur in pairs where one is higher than the other. In addition, the true maximum is, which is defined by a constraint boundary.Figure 3.4 shows the surface variations of the multi-peaked function. If we use 50 roaming ants and let them move around for 25 iterations, then the pheromone concentrations (also equivalent to the paths of ants) are displayed in Fig. 3.4. We can see that the highest pheromoneconcentration within the constraint boundary corresponds to the optimal solution.It is worth pointing out that ant colony algorithms are the right tool for combinatorial and discrete optimization. They have the advantages over other stochastic algorithms such as genetic algorithms and simulated annealing in dealing with dynamical network routing problems.For continuous decision variables, its performance is still under active research. For the present example, it took about 1500 evaluations of the objective function so as to find the global optima. This is not as efficient as other metaheuristic methods, especially comparing with particle swarm optimization. This is partly because the handling of the pheromone takes time. Is it possible to eliminate the pheromone and just use the roaming ants? The answer is yes. Particle swarm optimization is just the right kind of algorithm for such further modifications which will be discussed later in detail.第二部分中文翻译第二章遗传算法2.1 引言遗传算法是由John Holland和他的同事于二十世纪六七十年代提出的基于查尔斯·达尔文的自然选择学说而发展的一种生物进化的抽象模型。
Dept.ofComputerScience
Introduction
of the problem) can be reduced to the same problem for a di erent, normally smaller instance, called the kernel of the program. Second. the same works propose a novel graph representation of logic programs, called Extended Dependency Graphs (EDG), which allowed us to characterize stable models of a program in terms of distinguished 2-colorings of its EDG. These colorings are called admissible. In other words, there is a one-to-one correspondence between the answer sets of a kernel program Ker( ) and admissible 2-colorings of the related Extended Dependency Graph EDG(Ker( )). Therefore, the ASP problem is rephrased in terms of nding particular colorings of directed graphs; this problem will be called Admissible Coloring Problem (ACP). Since the ASP problem is NP-complete, so is ACP, hence exact solutions to ACP cannot be found in polynomial time (of course, in the worst case). Our approach, described in the rest of the paper, is to relax the problem to that of optimizing a suitable objective function (called tness from now on). More precisely, we try to solve ACP by means of a continuous model of computation. We propose: 1. to relax ACP to combinatorial optimization problem, by associating each instance of the problem to a suitable tness function, so that admissible colorings are exactly global maxima of every tness function, 2. to maximize a tness function by using the genetic model proposed in (Bertoni et al., 2000). In this model, the tness function corresponds to a discretetime system whose attractors are local maxima of the tness function itself. In this Section we show a reduction from ASP computation to a particular type of graph coloring introduced in (Brignoli et al., 1999). The rst step consists in reducing the original program to a new, smaller program Ker( ) which is equivalent to the original as far as existence and number of answer sets are concerned.
最新人工智能领域SCI期刊排名(20200814)
85
Systems
86
JOURNAL OF CHEMOMETRICS
87 ACM Transactions on Interactive Intelligent Systems
88
AI MAGAZINE
89
MACHINE VISION AND APPLICATIONS
90 Journal of Ambient Intelligence and Smart Environments
2
Information Fusion
IEEE TRANSACTIONS ON EVOLUTIONARY
3
COMPUTATION
4
MEDICAL IMAGE ANALYSIS
5
IEEE Transactions on Cybernetics
INTERNATIONAL JOURNAL OF INTELLIGENT
Category Scheme: WoS
Rank
Full Journal Title
Total Cites
Journal Impact Factor
Eigenfactor Score
IEEE TRANSACTIONS ON PATTERN ANALYSIS
1
AND MACHINE INTELLIGENCE
82
SYSTEMS
83
Genetic Programming and Evolvable Machines
ACM Transactions on Autonomous and Adaptive
84
Systems
International Journal on Semantic Web and Information
js07-期刊引证报告自科版2017(sci_jcr指标与分区)
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SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING ICGA JOURNALJournal of Web EngineeringPROGRAMMING AND COMPUTER SOFTWAREJOURNAL OF DATABASE MANAGEMENTIEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATIONIEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Cloud ComputingInformation FusionIEEE Transactions on Information Forensics and SecurityACM COMPUTING SURVEYSFuture Generation Computer Systems-The International Journal of eScience IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMSSwarm and Evolutionary ComputationHUMAN-COMPUTER INTERACTIONINFORMATION SYSTEMS FRONTIERSIEEE-ACM TRANSACTIONS ON NETWORKINGCOMMUNICATIONS OF THE ACMFOUNDATIONS OF COMPUTATIONAL MATHEMATICSINTERNATIONAL JOURNAL OF GENERAL SYSTEMSIEEE Cloud ComputingJournal of Grid ComputingFUZZY SETS AND SYSTEMSCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINEEVOLUTIONARY COMPUTATIONJOURNAL OF SYSTEMS AND SOFTWAREInternational Journal of Bio-Inspired ComputationSemantic WebINTERNATIONAL JOURNAL OF SYSTEMS SCIENCEIMAGE AND VISION COMPUTINGMULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSINGACM Transactions on Multimedia Computing Communications and Applications INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONSWiley Interdisciplinary Reviews-Data Mining and Knowledge DiscoveryJournal of Computational ScienceIEEE MULTIMEDIASTATISTICS AND COMPUTINGJOURNAL OF PARALLEL AND DISTRIBUTED COMPUTINGPERFORMANCE EVALUATIONACM TRANSACTIONS ON COMPUTER SYSTEMSJOURNAL OF THE ACMMULTIMEDIA SYSTEMSInternational Journal of Information SecurityCOMPUTINGCluster Computing-The Journal of Networks Software Tools and Applications QUANTUM INFORMATION & COMPUTATIONREAL-TIME SYSTEMSMULTIMEDIA TOOLS AND APPLICATIONSJOURNAL OF SUPERCOMPUTINGJOURNAL OF COMPUTER AND SYSTEM SCIENCESBig DataGenetic Programming and Evolvable MachinesEXPERT SYSTEMSACM Transactions on Autonomous and Adaptive SystemsCryptography and Communications-Discrete-Structures Boolean Functions and Sequences ACM Transactions on Architecture and Code OptimizationJOURNAL OF HEURISTICSCONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCEDESIGNS CODES AND CRYPTOGRAPHYFrontiers of Computer ScienceMATHEMATICAL STRUCTURES IN COMPUTER SCIENCEINFORMATION AND COMPUTATIONJOURNAL OF UNIVERSAL COMPUTER SCIENCEJOURNAL OF CRYPTOLOGYMICROPROCESSORS AND MICROSYSTEMSInternational Journal of Unconventional ComputingIBM JOURNAL OF RESEARCH AND DEVELOPMENTPARALLEL COMPUTINGCONNECTION SCIENCEDISTRIBUTED AND PARALLEL DATABASESSIAM JOURNAL ON COMPUTINGARTIFICIAL LIFEINTERNATIONAL JOURNAL OF PARALLEL PROGRAMMINGIET Information SecurityTHEORY AND PRACTICE OF LOGIC PROGRAMMINGNatural ComputingCOMPUTER SYSTEMS SCIENCE AND ENGINEERINGDISTRIBUTED COMPUTINGFORMAL METHODS IN SYSTEM DESIGNCOMPUTER JOURNALApplied OntologyTHEORETICAL COMPUTER SCIENCEJOURNAL OF SYMBOLIC COMPUTATIONJOURNAL OF LOGIC AND COMPUTATIONINTERNATIONAL JOURNAL OF QUANTUM INFORMATIONACM Transactions on Computational LogicNEW GENERATION COMPUTINGCOMBINATORICS PROBABILITY & COMPUTINGDISCRETE & COMPUTATIONAL GEOMETRY。
生物计算机
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生物计算机的发展趋势
生物计算机最终会促使电脑与人脑的融合，目前最 新一代实验计算机正在模拟人类的大脑。英国剑桥 大学研究发现了“生物电路”，一些蛋白质的主要 功能不是构成生物的某些结构，而是用于传输和处 理信息。人们正努力寻找神经原与硅芯片之间的相 人们正努力寻找神经原与硅芯片之间的相 似处，研制基于神经网络的计算机。尽管目前研制 似处，研制基于神经网络的计算机 出来的最先进的神经网络拥有的智力还非常有限， 仿生计算机是未来发展之路。 但大多数科学家认为，仿生计算机是未来发展之路 仿生计算机是未来发展之路 国外有科学家预言，到2020年，运算速度更快的生 到2020年 物将取代硅芯片。 物将取代硅芯片。
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有机分子的优点
由于有机分子构成的生物化学元件的特殊性，从 而使有机计算机具有三大显著优点： 体积小， 体积小，功效高 以分子水平的线路为目标的生 物化学元件其大小可能达到几百埃（一万万分之 一厘米 ），1平方毫米的面积上可容数亿个电路， 比目前的电子计算机提高了上百倍。 使生物本身固有的自我修复机能 自我修复机能得到发挥，这样 自我修复机能 即使芯片出了故障也能自我修复，所以有机计算 可靠性很高。 机具有半永久性，可靠性很高 可靠性很高
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生物计算机的可能性
人类有一门学科叫仿生学 仿生学，即通过对自然界生物 仿生学 特性的研究与模仿，来达到为人类社会更好地服 例子如：通过研究蜻蜓的飞行 务的目的。典型的例子 例子 制造出了直升机 直升机；对青蛙眼睛的表面“视而不 直升机 见”，实际“明察秋毫”的认识，研制出了电子 电子 蛙眼；对蝙蝠没有视力，靠发出超声波来定向飞 蛙眼 行的特性研究，制造出了雷达、超声波定向仪 雷达、 雷达 超声波定向仪等； 对“变色龙”的研究，产生了隐身科学和保护色 隐身科学和保护色 的应用……
计算机电子通信等信息类SCI期刊大全
计算机类SCI分区数据WoS四区（cs.whu）序号刊名1 AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS2 JOURNAL OF COMMUNICATIONS AND NETWORKS3 International Journal of Network Management4 ETRI JOURNAL5 INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING6 Journal of Web Semantics7 R Journal8 Security and Communication Networks9 INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS10 QUEUEING SYSTEMS11 INFORMATICA12 Frontiers of Computer Science13 IET Information Security14 Ad Hoc & Sensor Wireless Networks15 JOURNAL OF COMPUTATIONAL BIOLOGY16 Journal on Multimodal User Interfaces17 INFORMATION TECHNOLOGY AND LIBRARIES18 MICROPROCESSORS AND MICROSYSTEMS19 ENGINEERING COMPUTATIONS20 ACM Transactions on Modeling and Computer Simulation21 ACTA INFORMATICA22 CONCURRENT ENGINEERING-RESEARCH AND APPLICATIONS23 INTEGRATION-THE VLSI JOURNAL24 INTERNATIONAL JOURNAL OF COOPERATIVE INFORMATION SYSTEMS25 INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE26 International Journal of Microwave and Wireless Technologies27 COMPUTATIONAL INTELLIGENCE28 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY29 WIRELESS PERSONAL COMMUNICATIONS30 JOURNAL OF VISUALIZATION31 Radioengineering32 IEEE TECHNOLOGY AND SOCIETY MAGAZINE33 ADVANCED ROBOTICS34 IETE JOURNAL OF RESEARCH35 Semiconductors and Semimetals36 China Communications37 International Journal on Document Analysis and Recognition38 International Journal of Humanoid Robotics39 TRANSPORTATION JOURNAL40 Journal of Signal Processing Systems for Signal Image and Video Technology41 AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFAC42 IET Computer Vision43 Journal of the Society for Information Display44 Intelligent Service Robotics45 SIGMOD RECORD46 CONNECTION SCIENCE47 INDUSTRIAL ROBOT-AN INTERNATIONAL JOURNAL48 Elektronika Ir Elektrotechnika49 ACM TRANSACTIONS ON DESIGN AUTOMATION OF ELECTRONIC SYSTEMS50 JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS51 Mobile Information Systems52 Journal of Applied Logic53 Computer Science and Information Systems54 IEICE TRANSACTIONS ON COMMUNICATIONS55 Statistical Analysis and Data Mining56 Computers and Concrete57 AI MAGAZINE58 KYBERNETES59 Journal of Ambient Intelligence and Smart Environments60 ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE61 Advances in Mathematics of Communications62 Information Retrieval Journal63 Advances in Computers64 Research in Transportation Economics65 International Journal on Artificial Intelligence Tools66 Natural Computing67 MODELING IDENTIFICATION AND CONTROL68 Intelligent Data Analysis69 Journal of Simulation70 IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE71 Journal of Zhejiang University-SCIENCE C-Computers & Electronics72 Computational and Mathematical Organization Theory73 INTERNATIONAL JOURNAL OF APPLIED ELECTROMAGNETICS AND MECHANICS74 INTERNATIONAL JOURNAL OF QUANTUM INFORMATION75 ENGINEERING WITH COMPUTERS76 Journal of Organizational and End User Computing77 New Review of Hypermedia and Multimedia78 JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION79 JOURNAL OF ELECTRONIC IMAGING80 Biologically Inspired Cognitive Architectures81 PRESENCE-TELEOPERATORS AND VIRTUAL ENVIRONMENTS82 INFORMATION PROCESSING LETTERS83 INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING84 JOURNAL OF COMPUTATIONAL ACOUSTICS85 Language Resources and Evaluation86 MICROELECTRONICS INTERNATIONAL87 ALGORITHMICA88 IET Software89 Current Computer-Aided Drug Design90 MICROWAVE AND OPTICAL TECHNOLOGY LETTERS91 MATHEMATICAL STRUCTURES IN COMPUTER SCIENCE92 INTERNATIONAL JOURNAL OF ELECTRONICS93 CIRCUIT WORLD94 International Journal of Data Warehousing and Mining95 DISCRETE & COMPUTATIONAL GEOMETRY96 International Arab Journal of Information Technology97 DISCRETE MATHEMATICS AND THEORETICAL COMPUTER SCIENCE98 SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNAT99 INTERNATIONAL JOURNAL OF GAME THEORY100 COMPUTER JOURNAL101 DISCRETE DYNAMICS IN NATURE AND SOCIETY102 Journal of Public Transportation103 Transportation Letters-The International Journal of Transportation Research 104 International Journal of Ad Hoc and Ubiquitous Computing105 THEORETICAL COMPUTER SCIENCE106 JOURNAL OF UNIVERSAL COMPUTER SCIENCE107 Journal of Cellular Automata108 COMPUTER APPLICATIONS IN ENGINEERING EDUCATION109 Journal of Logical and Algebraic Methods in Programming110 ENVIRONMENTAL AND ECOLOGICAL STATISTICS111 FUNDAMENTA INFORMATICAE112 Translator113 JOURNAL OF FUNCTIONAL PROGRAMMING114 JOURNAL OF COMPUTER INFORMATION SYSTEMS115 INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION116 CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES117 APPLICABLE ALGEBRA IN ENGINEERING COMMUNICATION AND COMPUTING118 International Journal of Web Services Research119 Logical Methods in Computer Science120 NEW GENERATION COMPUTING121 AI COMMUNICATIONS122 APPLIED ARTIFICIAL INTELLIGENCE123 ANNALS OF PURE AND APPLIED LOGIC124 JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS125 RENDICONTI DEL SEMINARIO MATEMATICO DELLA UNIVERSITA DI PADOVA126 THEORY OF COMPUTING SYSTEMS127 INTELLIGENT AUTOMATION AND SOFT COMPUTING128 Advances in Applied Clifford Algebras129 ZEITSCHRIFT FUR ANALYSIS UND IHRE ANWENDUNGEN130 Journal of Nonlinear and Convex Analysis131 JOURNAL OF COMPUTATIONAL MATHEMATICS132 Asian Journal of Communication133 International Journal of Sensor Networks134 Analysis and Mathematical Physics135 IEEE Latin America Transactions136 VIRTUAL REALITY137 Scientific Programming138 Journal of Noncommutative Geometry139 ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING140 Frontiers of Information Technology & Electronic Engineering141 INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AN 142 International Communication Gazette143 European Journal of Transport and Infrastructure Research144 Applications of Mathematics145 International Journal of Shipping and Transport Logistics146 JOURNAL OF COMPUTATIONAL ANALYSIS AND APPLICATIONS147 Complex Analysis and Operator Theory148 Journal of Computational and Theoretical Transport149 Malaysian Journal of Computer Science150 DYNAMICAL SYSTEMS-AN INTERNATIONAL JOURNAL151 JOURNAL OF MATHEMATICAL ECONOMICS152 RUSSIAN JOURNAL OF NUMERICAL ANALYSIS AND MATHEMATICAL MODELLING153 Advances in Electrical and Computer Engineering154 Problems of Information Transmission155 Journal of Web Engineering156 JOURNAL OF ORGANIZATIONAL COMPUTING AND ELECTRONIC COMMERCE157 Turkish Journal of Electrical Engineering and Computer Sciences158 JOURNAL OF MICROWAVE POWER AND ELECTROMAGNETIC ENERGY159 LOGIC JOURNAL OF THE IGPL160 STOCHASTIC ANALYSIS AND APPLICATIONS161 ELECTRICAL ENGINEERING162 JOURNAL OF MATHEMATICAL SOCIOLOGY163 Differential and Integral Equations164 ACM Transactions on Asian and Low-Resource Language Information Processing 165 Chinese Journal of Communication166 NETWORK-COMPUTATION IN NEURAL SYSTEMS167 Iranian Journal of Fuzzy Systems168 RAIRO-THEORETICAL INFORMATICS AND APPLICATIONS169 Journal of Systems Science & Complexity170 PROGRAM-ELECTRONIC LIBRARY AND INFORMATION SYSTEMS171 COMPUTATIONAL GEOMETRY-THEORY AND APPLICATIONS172 INFINITE DIMENSIONAL ANALYSIS QUANTUM PROBABILITY AND RELATED TOPICS173 Journal of Logic Language and Information174 Annals of Combinatorics175 ELECTRONIC JOURNAL OF COMBINATORICS176 Pacific Journal of Optimization177 Mathematical Control and Related Fields178 Journal of Pseudo-Differential Operators and Applications179 Journal of Systems Engineering and Electronics180 Journal of Electrical Engineering & Technology181 IEEJ Transactions on Electrical and Electronic Engineering182 DESIGN AUTOMATION FOR EMBEDDED SYSTEMS183 ELECTROMAGNETICS184 IET Computers and Digital Techniques185 Journal of Semiconductor Technology and Science186 MINDS AND MACHINES187 CHINESE JOURNAL OF ELECTRONICS188 Econometrics Journal189 Numerical Mathematics-Theory Methods and Applications190 IEICE TRANSACTIONS ON ELECTRONICS191 ACM Journal on Computing and Cultural Heritage192 Journal of Grey System193 Revista Iberoamericana de Automatica e Informatica Industrial194 DIFFERENTIAL GEOMETRY AND ITS APPLICATIONS195 Journal of Nanoelectronics and Optoelectronics196 JOURNAL OF ALGEBRA AND ITS APPLICATIONS197 COMPUTING AND INFORMATICS198 COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRI 199 Homology Homotopy and Applications200 JAPAN JOURNAL OF INDUSTRIAL AND APPLIED MATHEMATICS201 JOURNAL OF COMPUTER AND SYSTEMS SCIENCES INTERNATIONAL202 Social Semiotics203 Journal of Electrical Engineering-Elektrotechnicky Casopis204 JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS205 INFORMACIJE MIDEM-JOURNAL OF MICROELECTRONICS ELECTRONIC COMPONENTS AND MAT 206 Annals of Functional Analysis207 Information Technology and Control208 Discrete Optimization209 Continuum-Journal of Media & Cultural Studies210 JOURNAL OF INFORMATION SCIENCE AND ENGINEERING211 Journal of Transportation Safety & Security212 Revista de la Union Matematica Argentina213 International Journal of Wavelets Multiresolution and Information Processin 214 FREQUENZ215 Fixed Point Theory216 JOURNAL OF DATABASE MANAGEMENT217 QUARTERLY JOURNAL OF SPEECH218 INTEGRATED FERROELECTRICS219 Milan Journal of Mathematics220 IEICE Electronics Express221 Computational Methods and Function Theory222 KSII Transactions on Internet and Information Systems223 Journal of Function Spaces224 FUNCTIONAL ANALYSIS AND ITS APPLICATIONS225 Communication Culture & Critique226 Text & Talk227 ACTA MATHEMATICA SINICA-ENGLISH SERIES228 JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS229 APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL230 Statistics and Its Interface231 COMPUTATIONAL COMPLEXITY232 JOURNAL OF THE KOREAN MATHEMATICAL SOCIETY233 MATHEMATICAL AND COMPUTER MODELLING OF DYNAMICAL SYSTEMS234 Proceedings of the Steklov Institute of Mathematics235 Journal of Mathematics and Music236 Revista Internacional de Metodos Numericos para Calculo y Diseno en Ingenie 237 East Asian Journal on Applied Mathematics238 NATURAL RESOURCE MODELING239 COMPUTER ANIMATION AND VIRTUAL WORLDS240 MATHEMATICAL SOCIAL SCIENCES241 Analele Stiintifice ale Universitatii Ovidius Constanta-Seria Matematica 242 Journal of Mass Media Ethics243 Theory and Applications of Categories244 Mathematics and Financial Economics245 Periodica Mathematica Hungarica246 JAVNOST-THE PUBLIC247 IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS248 COMPUTER MUSIC JOURNAL249 Journal of Numerical Mathematics250 Funkcialaj Ekvacioj-Serio Internacia251 Neural Network World252 INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE253 Automatika254 KYBERNETIKA255 TOPOLOGY AND ITS APPLICATIONS256 INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION257 JOURNAL OF MULTIPLE-VALUED LOGIC AND SOFT COMPUTING258 Romanian Journal of Information Science and Technology259 PMM JOURNAL OF APPLIED MATHEMATICS AND MECHANICS260 COMPUTER SYSTEMS SCIENCE AND ENGINEERING261 Media International Australia262 ALGEBRA COLLOQUIUM263 CMC-Computers Materials & Continua264 ACM SIGPLAN NOTICES265 Advances in Difference Equations266 Iranian Journal of Science and Technology-Transactions of Electrical Engine 267 Rhetoric Society Quarterly268 Glasnik Matematicki269 NARRATIVE INQUIRY270 Mathematical Communications271 ARCHIVE FOR HISTORY OF EXACT SCIENCES272 JOURNAL OF APPLIED COMMUNICATION RESEARCH273 Bollettino di Storia delle Scienze Matematiche274 Economic Computation and Economic Cybernetics Studies and Research275 INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 276 DYNAMIC SYSTEMS AND APPLICATIONS277 Mathematical Population Studies278 University Politehnica of Bucharest Scientific Bulletin-Series A-Applied Ma 279 IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUT 280 UTILITAS MATHEMATICA281 HISTORIA MATHEMATICA282 MICROWAVE JOURNAL283 CRYPTOLOGIA284 Applied Mathematics-A Journal of Chinese Universities Series B285 Acta Mathematicae Applicatae Sinica-English Series286 PROGRAMMING AND COMPUTER SOFTWARE287 Ukrainian Mathematical Journal288 International Journal of Transport Economics289 JOURNAL OF MEDIA ECONOMICS290 Electronics and Communications in Japan291 FUJITSU SCIENTIFIC & TECHNICAL JOURNAL292 INFOR293 ELECTRICAL ENGINEERING IN JAPAN294 African Journalism Studies295 Tijdschrift voor Communicatiewetenschap296 Journal of African Media Studies297 ICGA JOURNAL298 Pure and Applied Mathematics Quarterly299 Light & Engineering300 EPE Journal301 SOLID STATE TECHNOLOGY302 Journal of the Institute of Telecommunications Professionals303 Traitement du Signal304 ELECTRONICS WORLD305 Road & Transport Research306 IEEE Transactions on Cognitive and Developmental Systems引用次数影响因子eigenFactorScore 1506 1.1470.00259826 1.130.00268225 1.1180.000261153 1.1160.00199354 1.0790.000471091 1.0750.00154422 1.0750.002561095 1.0670.002551474 1.0660.002631335 1.060.00221398 1.0560.00071405 1.0390.00113355 1.0370.00082311 1.0340.000683216 1.0320.00826197 1.0310.00048260 1.0290.00039691 1.0250.00111406 1.010.0019363810.0010278310.0003749810.000350410.0007724610.0001511650.9940.001513580.9760.001096280.9640.000949110.9560.0021335080.9510.007044170.950.0008911340.9450.001912590.9430.0003915720.920.002442950.9090.000334380.9090.000287350.9030.001784610.9020.000525580.90.000644640.8970.000216800.8930.001555770.8850.000584870.8780.0011510280.8770.00151340.8750.0003513140.8750.001413580.8670.000279300.8630.000897700.8590.001266140.850.0011615580.850.002752610.8490.000362830.8380.000733920.8370.00093 17340.8270.00279 3750.8270.00196 4040.8130.00085 12310.8120.00113 7850.8110.00071 2620.8090.00033 8330.8070.00113 2540.80.00168 9270.7910.00103 3510.7890.00048 6690.7810.00166 4220.7780.00063 6630.7780.00084 3530.7750.00031 8220.7720.00113 3610.7720.00063 8860.7710.00099 3230.770.00081 3640.7690.00031 9620.7690.00137 8180.7660.00162 8530.7650.00137 1380.7595.00E-05 1410.7580.00018 19260.7570.00654 18130.7540.00261 1610.7530.00029 16520.750.00054 36180.7480.00625 5970.7460.00106 4540.740.00067 4400.7380.00104 1940.7370.00029 19870.7350.00516 2280.7330.0004 3860.7320.00052 51150.7310.00754 5620.730.00145 12450.7290.00109 2260.7270.00024 2190.7270.00019 17270.7240.00645 5020.7240.0003 3350.7230.00176 12930.720.00137 13120.7130.00235 32140.7110.00301 13590.7110.00349 5440.710.00063 1860.7060.00048 3480.7050.00064 73280.6980.01629 10670.6960.001127860.6880.00125 17110.6870.0028 2360.6860.00028 4050.6770.00081 7300.6750.00073 3320.6740.00046 14080.670.0028 3700.6670.00099 1340.6670.00024 5070.6610.00337 3510.6570.0003 3620.6540.00098 5880.6520.00049 11180.6470.0037 3710.6470.00055 4130.6460.00131 6630.6450.0029 3390.6440.00041 4020.6430.00078 3650.6430.00081 5790.6420.00151 6830.6410.00128 3470.6380.00057 3050.6350.00038 620.6320.00082 10060.6310.00102 3640.6280.00031 3430.6270.00047 2080.6250.00235 13240.6230.00216 880.6220.00016 3820.6220.00065 4860.6220.00103 3610.6190.00061 3240.6180.00058 1730.6090.00048 5730.6090.00097 2980.6050.00209 3230.60.00042 890.68.00E-05 4550.5970.00124 11650.5970.00354 2060.5970.00047 3360.5950.00038 6580.5810.00081 1060.580.00019 2670.5790.00023 6860.5780.00107 4490.5750.00025 3730.5750.0015 7600.5730.00199120.5620 1290.5620.00043 6380.5620.00024 2760.560.00063 2100.5580.00067 5590.5560.00106 3300.5560.00037 7710.5480.00163 8420.5480.00142 2480.5480.00036 4010.5440.0022 18450.5430.01147 3490.5430.00127 1040.5420.00125 840.5290.00056 8250.5290.00152 7680.5250.00183 4890.5170.00076 1390.5160.00035 4100.5160.00058 2310.5150.00049 2750.5150.00073 3580.5140.00045 5290.5130.00075 7910.5130.00287 1900.5090.00091 10750.5030.00156 900.50.00019 3560.50.0002 1440.50.00028 5980.4970.00346 4200.4970.00073 6020.4890.00428 3380.4880.00053 7110.4870.00121 2470.4860.00149 3170.4860.00078 3620.4840.00051 3440.4840.00072 4170.4830.00043 5880.4810.00075 950.4780.00016 1370.4770.00098 1630.4750.00031 4580.4690.00161 4960.4680.00091 6340.4680.00089 930.4650.00034 870.4640.00046 3270.4630.00053 2070.4620.000259130.4570.00109 2670.4570.0012 9790.4560.00189 2500.4550.00111 5690.4520.00119 1500.4510.00068 18410.450.00144 2090.4480.00071 2970.4480.00083 14430.4460.0042 9350.4460.00076 4810.4440.00099 4210.4440.00201 6360.4410.00124 5780.4410.00194 3450.4390.00074 8600.4360.00318 530.4350.00011 840.4310.00017 600.4260.00037 3530.4260.0006 3670.4240.00099 7510.4230.00131 1440.4220.00052 2430.4190.00037 3030.4190.00145 1630.4150.00129 3440.4150.00079 1910.4130.00033 14480.4110.00196 4020.4050.00018 2760.4050.00163 5040.3940.00125 2510.3940.00023 6160.390.00185 1590.380.00041 8080.3790.00125 19520.3770.00551 1520.3750.0001 3320.3650.00087 1080.3650.0002 18640.3570.00117 1550.3480.00015 2640.3460.00068 3750.3430.00142 3150.3390.00093 25410.3350.0032 8490.3350.00233 220.333 5.00E-05 2040.3330.0003 2560.3280.000496920.3080.00072 150.30 1030.2990.00019 3450.2990.00054 2640.2980.00052 1700.2860.00021 1840.2790.00034 13810.2740.00196 4820.2610.00082 2210.2580.00028 3960.2580.00063 1600.2563.00E-05 1740.2470.00049 5380.2420.00106 1140.230.00017 6990.2280.00079 1890.220.00021 1870.2170.00017 1670.2020.00036 1730.1910.00022 3800.1890.00025 3980.1880.00033 120.1712.00E-05 420.1717.00E-05 660.1540.00018 630.1520.00028 2410.1490.00141 410.1183.00E-05 1010.0910.00011 1620.0820.000170.0780 720.0281.00E-05 450.0262.00E-05 1150.0210.00014 7-999.999 1.00E-05。
美国人工智能专业大学排名TOP20
美国⼈⼯智能专业⼤学排名TOP20 ⼈⼯智能时代正在朝我们的⽣活⾛来，很多⼤学开设了AI专业，那么美国有哪些AI专业⽐较好的⼤学呢?这是很多学⽣⽐较感兴趣的问题。
和店铺⼀起来看看美国⼈⼯智能专业⼤学排名TOP20，欢迎阅读。
20. 加州⼤学圣地亚哥分校University of California-San Diego B.S. in Computer Science: Artificial Intelligence Cluster 学费：$13,693每年 在UCSD，所有CS专业的学⽣都有机会能够将⼈⼯智能作为他们的⼀个专业⽅向。
在课程⽅⾯，UCSD提供包括搜索和推理，计算机视觉和图像处理等独⽴课程。
19. 佐治亚州⽴⼤学Georgia State University B.S. in Computer Science: Concentration in Graphics and Human-Computer Interaction; M.S. in Computer Science: Coursework in Database & Artificial Intelligence 学费： $15,609每年 很少有⼤学能像乔治亚州⽴⼤学⼀样提供本科和硕⼠学位的⼈⼯智能课程。
在这⾥，研究⽣们可以选择数据库与⼈⼯智能这样的课程来拓展他们的对于⼈⼯智能的知识储备。
⽽对于本科学⽣们来说，还有HCI这样能够在这个领域提供介绍型知识的课程内容。
18. 普渡⼤学Purdue University B.S. in Computer Science: Machine Intelligence Track 学费：$13,081每年 在普渡，CS专业的本科⽣都可以选择⼈⼯智能，数据挖掘，机器学习，机器⼈以及⼀系列类似的课程。
尽管⼈⼯智能⽅⾯的课程在美国⼤学中并不罕见，但是很少有学校能够像普渡⼀样为本科⽣也提供这么多的课程选择。
人工智能在生物学中的应用
人工智能在生物学中的应用在科技快速发展的今天，人工智能（Artificial Intelligence，AI）已经广泛应用于各个领域，其中生物学也不例外。
人工智能技术的进步为生物学研究提供了全新的思路和方法，为科学家们解开生物界的奥秘提供了有力的工具。
本文将从不同角度探讨人工智能在生物学中的应用，并展示它对于生物学研究的重要意义。
1. 基因组学与人工智能人工智能在基因组学领域发挥着重要作用。
通过机器学习和深度学习的技术，科学家们可以分析海量的基因组数据，识别基因与疾病之间的关联，并预测某些疾病的风险。
此外，人工智能还可以帮助科学家在基因组数据中发现新的功能基因、调控序列和其他重要元件，加速基因的注释和功能解读。
2. 生物图像处理与人工智能生物图像处理是生物学研究中另一个重要的方向，它涵盖了从显微镜图像到遥感图像的各种类型。
通过利用人工智能算法，科学家们可以自动识别、定位和分类生物图像中的各种结构和细胞。
这项技术不仅可以大幅度提高生物图像处理的效率，还可以帮助科学家们发现新的生物模式和结构，推动生物学的进一步发展。
3. 蛋白质结构预测与人工智能蛋白质是生物学中重要的分子，其结构与功能密切相关。
然而，通过实验手段解析蛋白质结构的过程复杂而耗时。
借助人工智能技术，科学家们可以通过分析大量的结构数据和序列信息，构建高效的蛋白质结构预测模型，实现对蛋白质结构的快速预测与模拟。
这一技术的突破将加速蛋白质相关疾病研究的进展，并为药物设计和开发提供更多线索。
4. 生物信息学数据库与人工智能生物信息学数据库是存储和管理生物学数据的重要工具。
利用人工智能的方法，研究人员可以分析生物信息学数据库中的数据，进行数据挖掘和发现新的生物学规律。
此外，人工智能还可以帮助构建和优化生物信息学数据库的结构和算法，提高数据库的管理和利用效率。
总结起来，人工智能在生物学中的应用涵盖了基因组学、生物图像处理、蛋白质结构预测和生物信息学数据库等众多领域。
计算机与药物碰撞出生命科学的新生机——记上海科技大学免疫化学研究所研究员白芳
创新之路56 科学中国人 2023年7月计算机与药物碰撞出生命科学的新生机——记上海科技大学免疫化学研究所研究员白芳 郑 心 李 双见到上海科技大学免疫化学研究所研究员白芳时，她刚刚从新冠病毒的侵扰中恢复，虽然嗓音还有些沙哑，但她十分清楚自己的科研使命，早早便恢复了工作：指导学生、分析数据、确立下一阶段的研究目标……作为上海科技大学生命科学与技术学院助理教授、信息科学与技术学院特聘教授及美国莱斯大学访问教授，她担负着多个院系的科研、教学或服务任务，把井井有条的科研日常当成了自己生活的主旋律。
听着她对计算机辅助药物设计领域满怀激情的介绍，仿佛科研一线的工作场景正在眼前徐徐展开……木铎金声树风骨据白芳介绍，计算机辅助药物设计的发展阶段起始于20世纪60年代，最初是由美国的科温·汉施（Corwin Hansch）教授提出的定量构效分析方法。
而到了21世纪初，基因组学、蛋白质组学、生物信息学的发展提供了大量潜在的疾病靶标，药物设计正式进入生物学驱动的后基因组时代。
2010年以来，系统生物学得到迅速发展，这进一步启发了网络药理学、系统药理学等新概念的诞生，为复杂疾病的精准治疗带来了希望。
而白芳便是在这个时候正式投身入这一研究领域的。
可以说，白芳是幸运的。
生而逢时，科技的飞速发展为她及其他生命科学领域的相关从业者提供了大有可为的广阔天地，特别是人工智能技术的兴起，为药物研发领域带来了前所未有的契机。
药物研发工作从始至终都是一个高投入、高风险却低产出的行业。
一款可以推及千家万户的新药，其开发时间就算保守估计也需要10年以上，投资成本更是难计其数。
不过也正是这种局面赋予了白芳不懈探索与拼搏的动力，进而得以形成了自己的科研责任观——勤朴忠诚、持之以恒。
在与白芳的交流中，听到最多的话是“时间不够用，技术发展太快”，这是她迫切工作的原动力所在，也是她潜心科研工作的生动反映。
在多年的求学与工作中，她一直恪守初心，将加速药物研发效率、提高成功率与可靠性奉为圭臬，目前已在蛋白质-蛋白质互作预测与调控研究，新分子实体药物（靶蛋白降解剂小分子，如P R O T A C分子等）的设计与药效预测方面多有成绩，在《美国科学院院报》（P N A S ）、《自然·通讯》（Nature Communications ）、《核酸研究》（Nucleic Acids Research ）、《化学科学》（Chemical Science ）等学术期刊上以通讯作者或者第一作者等身份发表学术论文50余篇。
介绍图灵的英语作文
图灵——计算机科学之父Alan Turing, a name that is synonymous with genius and innovation, revolutionized the field of computer science with his groundbreaking work. Born in 1912, Turing exhibited remarkable intelligence from a young age, displaying a profound understanding of mathematics and logic. His passion for problem-solving and quest for knowledge would eventually lead him to become one of the most influential figures in the history of computing.During his time at Cambridge University, Turing made significant contributions to the field of cryptography, breaking several important codes during World War II. His work was so significant that it helped alter the course of the war, demonstrating the incredible potential of his intellectual capabilities.After the war, Turing turned his attention to the fledgling field of computer science. He is credited with developing the theoretical framework for what we now know as the modern computer, laying the foundation for future technological advancements. His groundbreaking work on machine intelligence and artificial intelligence remainsinfluential today, shaping the way we understand andinteract with technology.Despite his numerous achievements, Turing's life wasnot without its challenges. He faced discrimination and persecution for his homosexuality, which ultimately led to his tragic death in 1954. However, his legacy lives on, and his contributions to computer science and beyond are remembered and celebrated worldwide.Alan Turing's impact on the world is immeasurable. His legacy as the father of computer science is one that will forever be remembered. His groundbreaking work has revolutionized the way we live and interact with technology, and his influence extends far beyond the realm of computing. His life and work are a testament to the power of the human mind and the impact that one individual can have on the course of history.**图灵——计算机科学之父**艾伦·图灵，这个名字与天才和创新紧密相连，他以其开创性的工作彻底改变了计算机科学领域。
生物计算机
未来的计算机摘要：目前，计算机工业飞速发展，计算机技术日益成熟。
然而晶体管的密度已接近当前技术的理论极限，发展空间似乎越来越小，因此人们不断寻找新的计算机结构。
为了实现高集成度，使计算机得到进一步发展，科学家们把目光转向了分子生物学方面，并借鉴生物界的各种处理问题的方式(生物算法)，提出了一些生物计算机的模型。
关键词：生物计算机；蛋白质分子；生物芯片；DNA生物计算机（biological computer）又称仿生计算机(bionic computer)。
以生物芯片取代在半导体硅片上集成效以万计的晶体管制成的计算机。
涉及计算机科学、脑科学、神经生物学、分子生物学、生物物理、生物工程、电子工程、物理学和化学等有关学科。
在过去的半个多世纪中，分子生物学将生命现象分解成大量基因和蛋白质的组成。
英国《自然》杂志报道，英国剑桥大学研究发现了“生物电路”。
他们对一种细菌中的蛋白质进行研究发现，细菌内部存在着由蛋白质构成的信息处理网络，该网络可根据分子密度和形状等性质的变化传递和处理信息，并根据接收到的信息而驱使细菌游向营养物质所在的地方。
同样，美国斯坦福大学的专家在细菌中也发现了“生物电路”，并在生物利用能量糖酵解过程中发现了-逻辑运算现象，找到了有关的“逻辑门”。
因此，人类可以利用遗传工程技术，仿制出这种蛋白质分子，用来作为元件制成计算机。
20世纪70年代以来，人们发现脱氧核糖核酸(DNA)处在不同的状态下，可产生有信息和无信息的变化。
联想到逻辑电路中的0与1、晶体管的通导或截止、电压的高或低、脉冲信号的有或无等等，科学家们激发了研制生物元件的灵感。
这项研究中最著名的代表就是美国著名的生物化学家、国际电子分子生物风险学会主席詹姆士·麦卡里尔博士，他不仅是生物分子电子学的创始人之一，而且他带领一个6人小组在华盛顿近郊的一座普通楼房里，进行着生物芯片和生物计算机的开拓性研究。
1982年，在法国秀丽的阿尔卑斯山上举行了首届生物计算机国际会议，来自世界各地的生物学家、化学家、物理学家、医学家、遗传学家、分子生物学家、微电子学家和计算机科学家们会聚一堂，探讨生物计算机的发展前景。
CI_Glimpse_143001978
Contents
LOGO
Topic 1:Computation Intelligence: A Glimpse Topic 2:Genetic Algorithm (遗传算法) Topic 3:Fuzzy Logic (模糊逻辑) Topic 4:Particle Swarm Optimization (粒子群优化算法) Topic 5: Bacterial Foraging Algorithm (细菌捕食算法) Topic 6: Memetic Algorithm Topic 7: Applications
SZU-TI DSPs Lab
Topic 1:Computational Intelligence: A Glimpse
LOGO
1.1 History 1.2 Magazine & Conference 1.3 People
SZU-TI DSPs Lab
Zhengyou Zhang Editor-in-chief (2008-2009)
SZU-TI DSPs Lab
Publications
LOGO
IEEE Transactions on Computational Intelligence and AI in Games The journal has a broad scope and publishes high quality papers on all aspects of computational intelligence and artificial intelligence related to games. The journal is co-sponsored by the IEEE Computational Intelligence Society, the IEEE Computer Society, the IEEE Consumer Electronics Society and the IEEE Sensors Council. It is technically co-sponsored by the IEEE Systems, Man, and Cybernetics Society, the IEEE Instrumentation and Measurement Society, the IEEE Robotics and Automation Society, and the IEEE Communications Society.
生物计算机
生物计算机
有机化合物存储数据的计算机
01 简介
03 缺点 05 发展进程 Nhomakorabea目录02 优点 04 种类 06 仿生应用
07 关键因素
09 新型产品
目录
08 研究方向
生物计算机也称仿生计算机，主要原材料是生物工程技术产生的蛋白质分子，并以此作为生物芯片来替代半 导体硅片，利用有机化合物存储数据。信息以波的形式传播，当波沿着蛋白质分子链传播时，会引起蛋白质分子 链中单键、双键结构顺序的变化。运算速度要比当今最新一代计算机快10万倍，它具有很强的抗电磁干扰能力， 并能彻底消除电路间的干扰。能量消耗仅相当于普通计算机的十亿分之一，且具有巨大的存储能力。生物计算机 具有生物体的一些特点，如能发挥生物本身的调节机能，自动修复芯片上发生的故障，还能模仿人脑的机制等
生物计算机是全球高科技领域最具活力和发展潜力的一门学科，该种计算机涉及多种学科领域，包括计算机 科学、脑科学、分子生物学、生物物理、生物工程、电子工程等有关学科。它的主要原材料是生物工程技术产生 的蛋白质分子，并以此作为生物芯片。生物计算机芯片本身还具有并行处理的功能，其运算速度要比当今最新一 代的计算机快10万倍，能量消耗仅相当于普通计算机的十亿分之一，存储信息的空间仅占百亿亿分之一。生物计 算机有很多优点，主要表现在以下几个方面：
Nature清华大学为类脑计算提出了新的系统层次结构
Nature清华大学为类脑计算提出了新的系统层次结构今天给大家介绍清华大学的Youhui Zhang团队在Nature 2020上发表的文章“A system hierarchy for brain-inspired computing”。
作者在文章中提出了“神经形态完备性”，放宽了类脑计算对硬件完备性的要求，还提出了相应的系统层次结构，其中包括图灵完备的软件抽象模型和通用的抽象神经形态架构。
1研究背景类脑计算为人工智能的发展提供了有前景的平台，与传统计算系统具有围绕图灵完备和冯·诺伊曼体系结构建立的计算机层次结构不同，类脑计算目前还没有通用的系统层次结构或类脑计算完备性理解。
这会影响软件和硬件之间的兼容性，从而降低编程灵活性和类脑计算的开发效率。
现有的研究试图通过特定领域的编程语言或框架来桥接各种软件和硬件，但是这些研究通常依赖图灵完备或不考虑完备性，很少有研究工作致力于解决硬件完备性、编程语言完备性和类脑计算的通用系统层次结构问题。
为此，作者提出了神经形态完备，这是一种针对类脑计算的更具适应性的完备性定义，它放宽了对神经形态硬件的完备性要求，可以通过引入新的维度（近似粒度）来改善不同硬件和软件设计之间的兼容性，并扩大设计空间。
此外，作者还提出了一种具有高度通用性类脑计算的系统层次结构，该结构分为三个层次：软件，硬件和编译。
图1直观地展示了类脑计算系统和传统计算系统的层次结构差别。
图1 类脑计算系统和传统计算系统的层次结构2神经形态完备对于任何给定的误差间隙ε≥0和任何图灵可计算函数f（x），如果计算系统对任意有效输入x均可实现函数F（x）使得“ F（x）-f （x）”≤ε，则认为这个计算系统是神经形态完备的。
神经形态完备性用于衡量神经形态计算系统的兼容性，它不再使用算法精确计算函数，而采用逼近计算，从而放宽了对完备性的要求。
神经形态完备性将通用逼近与通用可计算性联系在一起，为将图灵完备程序转换为神经形态完备系统上的等效可执行程序奠定了理论基础，从而扩大了完备硬件的范围。
计算生物学清华读博
计算生物学清华读博摘要：一、计算生物学简介1.计算生物学的定义2.计算生物学在生物科学中的重要性二、清华大学计算生物学专业读博简介1.清华大学计算生物学专业简介2.计算生物学博士生的学习内容与课程设置3.计算生物学博士生毕业后的发展前景三、读博期间的挑战与收获1.面临的学术挑战2.团队合作与交流3.科研成果与个人成长四、对计算生物学未来发展的展望1.计算生物学在我国的发展趋势2.计算生物学在生物科学领域的前景3.对计算生物学未来研究方向的展望正文：计算生物学是一门交叉学科，涉及生物学、计算机科学和数学等领域，通过利用计算机和数学方法研究生物学问题。
在我国，清华大学作为顶级高校，其在计算生物学领域的教育与研究一直走在前列。
本文将介绍计算生物学在清华的读博经历以及该领域的发展前景。
清华大学计算生物学专业致力于培养具有扎实生物学、计算机科学和数学基础的复合型人才。
博士生在学习期间，需要掌握生物学基础知识，如分子生物学、遗传学等，同时学习计算机科学相关技能，如编程、数据分析等。
课程设置涵盖了计算生物学的主要研究领域，包括基因调控网络、生物信息学、系统生物学等。
读博期间，计算生物学博士生面临着诸多挑战，如理解生物学现象背后的复杂机制、发展新的计算方法解决生物学问题等。
在这个过程中，团队合作与交流至关重要。
博士生们需与实验室其他成员紧密协作，共同解决研究难题。
此外，博士生还需参加国内外学术会议，与同行交流最新的研究成果，拓宽自己的视野。
计算生物学博士生在完成学业后，有着广阔的发展前景。
他们可以在学术界、企业界和政府部门等多个领域找到合适的工作岗位。
随着生物科学的发展，计算生物学在生物学研究中的作用越来越重要，对计算生物学人才的需求也不断增加。
计算生物学作为一门新兴交叉学科，在我国的发展趋势良好。
随着国家对科技创新的重视，计算生物学领域的研究经费不断增加，为该领域的发展提供了有力支持。
同时，计算生物学在生物科学领域具有广泛的应用前景，如基因编辑、药物设计等。
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Emergent order arises from simple local rules.
We sometimes exhibit swarm behaviour too
But we’re mainly interested in animals and insects
locusts
ants
Because we might learn something
Ants or wasps on a hunt for food, or on the attack, behave as if with a single mind, co-ordinating different actions with different parts of the swarm.
Biologically Inspired Computation
Lecture 5: Introducing Swarm Intelligence contents: the behavior of natural swarms and flocks -- Reynold’s rules and swarm simulation.
It also maybe helps with migration
If we can assume that:
– An individual has an idea, but not a perfect one, of where to go … e.g. by itself it may go a few degrees off course. – The “errors” of individuals are not correlated (i.e. they’re all wrong in a randomly different way) – An emergent result of the flocking is that the flock’s direction is the average of its members’ directions.
Some of the images in this lecture come from slides for a Course in Swarm Intelligence given at :
Swarms, and how they might inspire us
There are some interesting things that come to mind when we think of swarms (flocks, schools, etc …): • A swarm sometimes seems to behave as if it is an individual organism.
Two main things that come from swarm inspiration:
Optimisation algorithms. Ants seem to find the shortest path to find food that may be quite distant from their nest. They do this via “stigmergy” – laying pheronomones on their path as they move. This has directly inspired the design of a very successful optimisation method. Meanwhile, the adaptive culture model has led to a different, and also very successful, new optimisation algorithm
Back to computer science …
From the CS viewpoint, the question is: How does this kind of , apparently organised, group behaviour emerge, without a central controller? Without (like we have) something like a brain in control of everything? The emergent behaviour that we see arises purely as a result of individuals in the swarm processing information in their (fairly) immediate neighbourhood. So, studying this in nature suggests how we can get co-ordinated behaviour from a group of individuals, without having to specify any overall controller. This is very useful, for example, for designing computer networks. If one main machine was in control of the network, and that machine crashed, … But so far that has not been a main success area for swarm inspiration …
So …
Flocking occurs so much because it is clearly useful. But how do they do it so well? Individual ants are not clever enough to understand the benefits. It comes down to: simple behaviours of individuals in a group can have useful emergent properties. A theme we will continue to see a lot …
How on earth can these things happen? HERE IS A LIVE EXAMPLE
Two simple rules: While continually wandering randomly: If you are empty-handed and encounter a disc, pick it up If you are carrying a disc and encounter another of the same colour, put yours down.
Other puzzling things that swarms do
• Termites build huge nests – how?? Is an individual termite clever enough to do this?
• Bees build hives, with complex internal structure -- same question.
The Adaptive Culture Model
Robert Axelrod has a well-known theory, “Axelrod’s Culture Model”, which explains how ideas spread in societies. Kennedy and Eberhart (a computer scientist and a social scientist respectively) altered this into the “Adaptive Culture Model”, which works like this: If you think your neighbour is good, then be more like them. And that’s basically it. But notice the important words, neighbour: you change yourself under the influence of people nearby good: in some way your neighbour is more optimal than you, otherwise why be like them? more like: this is vague, so you have freedom in how you change This is actually a very good model for how culture and ideas spread quickly in societies. Everything from rumours to eating habits. I only hope this works with `green’ behaviour …
Another kinds of swarm behaviour is the dynamics and evolution of ideas as they get passed on and changed in social networks.
ቤተ መጻሕፍቲ ባይዱA recommends to B, B recommends to C, …
• A swarm, of ants/bees/locusts/etc often exhibits behaviours that seem clearly more intelligent than any of the individual members of it.