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Description: 人工股市（Artificial Stock Market，简称ASM）是模拟股市运作的一个程序。在这个电脑中的虚拟市场中，若干被称为交易者的人工智能程序(Agent)通过观察它们所在的数字世界中股价和股息的不断变换而做出预测，并且根据这些预测做出购买股票与否以及购买股票数量的决策。反过来，所有的交易者的决策又决定了股票的价格，这样，整个的股票交易市场就构成了一个自我封闭的计算系统。同时，这些交易者都具有学习的能力，可以根据以前预测的成功或者失败对自己的决策进行调整，并且通过一种被称为遗传算法的方法产生创新能力。总之，ASM是一个电脑中不断进化的虚拟股票市场！通过研究ASM，我们可以更好的理解现实世界的行为，并且它提供了一个很好的现实股市的隐喻。 备注：该程序运行需要有Swarm平台的支持，关于这个平台可以到http://www.swarm.org免费下载。 -artificial stock market (Artificial Stock Market, or ASM) is a stock market simulation operation of the procedure. In this computer in the virtual market, a number of traders known as artificial intelligence (Agent) by observing them where the digital world and the stock dividend and the constantly changing projections, and based on these projections made whether or not to purchase stocks and the purchase of shares in decision-making. Conversely, all the decision-making transactions in turn determines the share price, so that the entire stock market would constitute a self-enclosed computer system. Moreover, these transactions are capable of learning, can be predicted based on previous success or failure of their decisions for the adjustment, and through a genetic algorithm known as the me
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Author: 张洁 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhar博士和kennedy博士发明。源于对鸟群捕食的行为研究 ，PSO同遗传算法类似，是一种基于叠代的优化工具。
-Particle Swarm Optimization (PSO) is an evolutionary computation technique (evolutionary computation), and has Eberhar Dr. Dr. kennedy invention. Stems from the behavior of predatory birds, PSO with genetic algorithm is similar to an iterative optimization-based tools.
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Author: 叶开 |
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Description: 粒子群算法与遗传算法用于优化的问题求解,可以解决一些-Particle Swarm Optimization and Genetic Algorithms for optimization problem solving, you can solve some
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Author: tiger |
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Description: vc++版本的粒子群优化算法程序,该算法使用简单,收敛快速,是遗传算法之后的又一重要智能算法.-vc++ version of particle swarm optimization procedure, the algorithm uses a simple, rapid convergence, after the genetic algorithm is another important intelligent algorithm.
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Author: 江南 |
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Description: 将粒子群算法与免疫结合，计算函数的优化极值。-The particle swarm optimization combined with the immune calculated extremal function optimization.
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Author: 刘洋 |
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Description: 遗传粒子群的最新改进算法，随着维度增加效果更好-Genetic Particle Swarm latest improved algorithm, with the increasing dimensions better
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Author: huangchao |
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Description: 微粒群算法[PSO ] 是由Kennedy 和Eberhart等于1995 年开发的一种演化计算技术, 来源于对鸟群捕食过程的模拟。PSO同遗传算法类似，是一种基于叠代的优化工具，但与遗传算法使用遗传操作子进行优化不同，利用群体中各个体之间的“协作”与“竞争”关系，根据自身及其竞争者的飞行经验，调整自己的行为。同遗传算法比较，PSO的优势在于简单容易实现并且没有许多参数需要调整。目前已广泛应用于函数优化，神经网络训练，模糊系统控制以及其他遗传算法的应用领域。-Particle Swarm Optimization [PSO] are equal by Kennedy and Eberhart in 1995 developed an evolutionary computing technology, from preying on the birds of the simulation process. PSO with genetic algorithm is similar to an iterative optimization-based tool, but the use of genetic algorithms and genetic manipulation of different sub-optimize the use of groups between the various entities within the " collaboration" and " competitive" relationship, according to themselves and their competition the flying experience, adjust their behavior. Comparison with genetic algorithms, PSO has the advantage of being simple and easy and did not realize the need to adjust the parameters much. Has been widely applied to function optimization, neural network training, fuzzy system control, as well as other genetic algorithm applications.
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Author: wzy |
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Description: 一系列好用的用户友好的启发式优化算法，包括非自适应算法，基于模拟退火算法的种群算法，基本遗传算法，差分进化算法以及粒子群优化算法。此外，也包括神圣算法，它利用了所有这些优化算子，虽然有时交换种群之间的不同算法。-A nice set of user-friendly heuristic optimizers. Included are a non-adaptive, population based Simulated Annealing algorithm, a basic Genetic Algorithm, (transversal) Differential Evolution algorithm and Particle Swarm Optimization algorithm. Also, the GODLIKE-algorithm is included, which simply uses all of these optimizers while occasionally swapping populations between the different algorithms.
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Author: 竹子的信仰 |
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Description: 粒子群算法与遗传算法的结合研究，值得参考！-Particle Swarm Optimization and Genetic Algorithm for the combination of research
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Author: 和子 |
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Description: 关于一些算法中如何生成新成员的选择方法，粒子群算法，遗传算法等算法可以用。-How about some algorithm to generate a new member of the selection method, particle swarm optimization, genetic algorithms and other algorithms can be used.
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Author: wang |
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Description: ：目前，粒子群算法在连续问题优化上的应用已经很广泛，然而在离散问题优化方面仍处在尝试阶段。提出了一种改
进粒子群算法来解决矩形件排样优化问题(离散优化问题)。该算法融合了遗传算法中的交叉和变异思想，采用了信息交流
策略，使其达到快速优化目的。算法也对“最低水平线法”解码方式进行了改进。实验结果表明，该算法具有快速、高效特点，
与现有同类算法比较，在解决矩形件排样问题方面的优势明显。-: At present, the particle swarm optimization algorithm in a row on the issue has very broad applications, but in the discrete optimization problem is still trying to stage. An improved particle swarm optimization to solve the optimization of the rectangular piece nesting problems (discrete optimization problem). The algorithm combines the genetic algorithm crossover and mutation in the thinking of information-sharing strategies used to achieve the purpose of rapid optimization. Algorithm of the " minimum horizontal method" approach to the improvement of decoding. Experimental results show that the algorithm is fast, high-performance characteristics compared with existing similar algorithm, in solving rectangular piece nesting problems have obvious advantages.
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Author: sdkj |
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Description: 此包含有遗传算法、粒子群算法、BP算法优化对角递归神经网络的MATLAB程序-This includes genetic algorithms, particle swarm optimization, BP algorithm for diagonal recurrent neural network of the MATLAB program
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Description: 优化问题是工业设计中经常遇到的问题,许多问题最后都可以归结为优化问题. 为了解决各种各样的优化问题,人们提出了许多优化算法,比较著名的有爬山法、遗传算法等.-Industrial design optimization problem is often encountered problems, many problems can be attributed to the final optimization problem. In order to solve a wide range of optimization problems, it put forward a number of optimization algorithms, there are relatively well-known climbing method, such as genetic algorithms.
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Author: 刘文 |
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Description: 粒子群算法、遗传算法优化RBF径向基神经网络。-Particle swarm optimization, genetic algorithm optimization of RBF Radial Basis Function Neural Networks.
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Author: hexing |
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Description: 利用vc++编程调用ansys对模型进行修正.使用的是带遗传算法的粒子群优化算法.-Using vc++ programming model called the amendment ansys. Using a genetic algorithm with particle swarm optimization algorithm.
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Author: xiaozhutou999 |
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Description: 用于求解约束优化问题的算法，算法为差分进化/遗传算法/微粒群算法的融合。对于“[7] T. P. Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000”中给出的13个标准测试函数，均能得到问题最优解。如有任何疑问，请于http://2shi.phphubei.com.cn/index.php发帖询问，本人将详细解答。-Solving constrained optimization problems for the algorithm, Algorithm for Differential Evolution/Genetic Algorithm/Particle Swarm Optimization integration. For " [7] TP Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., Vol. 4, no. 3, pp. 284-294, Sep. 2000" given in 13 standard test functions, the optimal solution can be the problem. If you have any questions, please post http://2shi.phphubei.com.cn/index.php asked, I will answer in detail.
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Author: 李剑 |
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Description: 采用遗传微粒群算法（GPSO）求解旅行商问题（TSP）的源代码。内附多个算例，本算法对于中小规模问题求解效率很高，对于大规模问题则效率略低。如有任何疑问，请于http://2shi.phphubei.com.cn/index.php发帖询问。-Genetic Particle Swarm Optimization (GPSO) for Traveling Salesman Problem (TSP) of the source code. Containing a number of examples, the algorithm for solving small and medium-sized high efficiency, large-scale problems for the slightly lower efficiency. If you have any questions, please post http://2shi.phphubei.com.cn/index.php asked.
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Author: 李剑 |
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Description: genetic algorithm, simulated annealing, singleobjective, particle swarm optimi..., optimization, classes
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Author: eedee |
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Description: matlab程序（粒子群，遗传算法，牛顿迭代）-matlab program (particle swarm, genetic algorithm, Newton iteration)
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Author: 杨超 |
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Description: 粒子群优化算起源于对鸟群、鱼群以及对某些社会行为的模拟，是一种基于群体智能的进化计算技术。而小生境技术则起源于遗传算法，这种方法能使基于群体的随机优化算法形成物种，从而使相应的优化算法具有发现多个最优解的能力。而多分类器集成技术则是通过多个分类器进行某种组合来决定最终的分类，以取得比单个分类器更好的性能。多分类器集成技术要求基元分类器不仅个体性能要好并且其差异度要大，这与小生境技术形成物种的能力具有很多内在的相似性。目前己经有研究者将小生境技术应用于多分类器集成，但由于传统的小生境技术仍然不完善，存在一些内在的陷，因而这些应用还不成熟和完善。
(Particle swarm optimization (partieleSwarmOptimization) originated in the birds, fish, and of a
Some simulation of social behavior, is a swarm intelligence-based evolutionary computing. The origin of the niche technology is In genetic algorithms, this method can make random optimization algorithm based on the formation of groups of species, so that the appropriate priority
Algorithm has the ability to find multiple optimal solutions. The integration technology of multiple classifiers is through multiple classifiers into
Some combination of the line to determine the final classification, in order to obtain better than a single classifier performance. Integration of multiple classifiers
Technical requirements for primitive classification is not only better individual performance and the difference to a large degree, which form a niche technology The ability of species has many inherent similarities. The researchers will now have a niche technology used in multisection
Class ens)
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Author: dreamer |
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