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Description: This a very simple baseband simulator for SC-FDMA system. This simulator is part of the upcoming book “Single Carrier FDMA: A New Air Interface for Long Term Evolution” (Wiley, Nov. 2008) which I co-authored with professor David J. Goodman at Polytechnic University.
The purpose of this simulator is to give some concrete idea of how SC-FDMA system works. It does lack many realistic and sophisticated features such as channel coding, time-varying fading channel model, soft decision decoding, etc. Regardless, I am hoping that it will help you understand SC-FDMA which is a fairly new development in 3GPP LTE.
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Author: jeff.kuo |
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Description: 使用java编写的GUI的黑白棋游戏，搜索算法采用经典的博弈树，并在此基础上做了大量优化，我的评估函数采用了Simon M. Lucas 和 Thomas P. Runarsson 在其合作发表的 Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation 中通过对比即时差分学习（TDL，Temporal Difference
Learning）和协同进化（CEL，Co-Evolution）对于计算黑白棋的估值函数时得到的一个最佳 WPC，估值函数同时包括行动力的计算。于此同时，对于计算可行位置，我采用了基于查表的方法。对于开局，我使用了 Kyung-Joong Kim 和 Sung-Bae Cho 的 Evolutionary Othello Players Boosted by Opening Knowledge 使用的 99 种 well-defined 的开局表。
总体来说，AI智能很强，默认等级一般人是下不过它的。-GUI using java prepared Riversi games, search algorithms using classical game tree, and on this basis have done a lot of optimization, the evaluation function I used Simon M. Lucas and Thomas P. Runarsson published in its Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation in the difference by comparing the real-time learning (TDL, Temporal Difference
Learning) and co-evolution (CEL, Co-Evolution) Smileys for the calculation of the valuation function to be one of the best of the WPC, the valuation function at the same time including the calculation of mobility. At the same time, a viable location for the calculation, I used the method based on the look-up table. For start, I used the Kyung-Joong Kim and Sung-Bae Cho s Evolutionary Othello Players Boosted by Opening Knowledge use of 99 kinds of well-defined start of the table.
Generally speaking, AI very smart, the default level, however most people are under it.
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Author: da |
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Description: 用Java实现的进化算法包。包括遗传算法、粒子群算法、memetic算法和进化策略算法。-evolutionary-algorithm
Evolutionary Algorithm package implemented using Java.
The package serves as a foundation class library, supporting the implementation many variants of Evolutionary Algorithms, currently including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Memetic Algorithm (MA), Evolution Strategy (ES).
Highlighted features
· Support both binary & real-coded string representations of solution
· Operator-based design for flexibility
· EA Operators: Selection, Crossover, Mutation, Move operators in PSO & and the adaptive scheme in EA
· Individual learning: Davidon–Fletcher–Powell (DFP) and Davies, Swann, and Campey with Gram-Schmidt orthogonalization (DSCG) strategies and Random Mutation Hill-climbing (RMHC)
In addition, algorithm pipeline which is specified by XML file is also provided for practitioner to configure & design evolutionary algorithms at ease. User can edit runtime & algorithm parameters in the configuration file (XML) & issue the co
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Author: 陈雷 |
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Description: 使用java編寫的GUI的黑白棋遊戲-GUI using java prepared Riversi games, search algorithms using classical game tree, and on this basis have done a lot of optimization, the evaluation function I used Simon M. Lucas and Thomas P. Runarsson published in its Temporal Difference Learning Versus Co-Evolution for Acquiring Othello Position Evaluation in the difference by comparing the real-time learning (TDL, Temporal Difference Learning) and co-evolution (CEL, Co-Evolution) Smileys for the calculation of the valuation function to be one of the best of the WPC , the valuation function at the same time including the calculation of mobility. At the same time, a viable location for the calculation, I used the method based on the look-up table. For start, I used the Kyung-Joong Kim and Sung-Bae Cho s Evolutionary Othello Players Boosted by Opening Knowledge use of 99 kinds of well-defined start of the table. Generally speaking, AI very smart, the default level, however most people are under it.
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Author: chowkwing |
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Description: 利用遗传算法对两个神经网络模块共同进化的例子，此方法优于对两个模块分别进化-Using the genetic algorithm neural network module of the two examples of co-evolution, this method is superior to the evolution of the two modules were
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Author: tjy |
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Description: Using Competetive Co-Evolution to Evolve Better Pattern Recognizers
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Author: stonelee |
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Description: 在MATLAB环境下编的基于邻居搜索的动态差分协同进化代码-Neighbor search based on dynamic differential co-evolution code
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Author: liuhuaxian |
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Description: 标准合作型协同进化遗传算法matlab源程序，对函数进行优化，计算。来源：巩敦卫，孙晓燕.协同进化遗传算法理论及应用[M],北京，科学出版社，2009-a co-opertative co-evolution genetic algorithm
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Author: xxbb1234 |
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Description: 将遗传算法的改进协同进化遗传算法应用于机器人路径的规划中-Improved genetic algorithm co-evolution genetic algorithm in robot path planning
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Author: liujinying |
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Description: 采用合作式协同进化算法 用于解决简单的函数优化问题-The cooperative co-evolution algorithm for solving the simple function optimization problem
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Author: jenny |
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Description: 协同进化的经典历程 适合初学者学习使用 matlab编写 简单经典-The co-evolution of the classic course is suitable for beginners to learn to use Matlab prepared a simple classic
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Author: 冯 |
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Description: 利用差分分组的协同进化算法，适合大规模优化算法，C语言实现-Cooperative Co-evolution with Differential Grouping for Large Scale Optimization
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Author: Steve Park |
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Description: 本书包括绪论、GP机理研究及算法性能改进技术、GP自定义函数共同进化方法研究、GP适应度函数光滑拟合与调整参数方法研究、基于GP的多目标决策函数建模及稳定性分析等-The book includes an introduction, GP mechanism and algorithm performance improvement techniques, GP custom function study co-evolution method, GP fitness function smooth fitting and adjusting the parameters studied, based on multi-objective decision modeling and stability analysis functions such as GP
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Author: ja |
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Description: AK工具箱基于Matlab，用于蛋白质多序列比对的共进化分析。
前言
AK工具箱的目标是提供一套独立于Matlab生物信息学工具箱的Matlab函数以对蛋白质多序列比对进行高效的共进化分析。目前，本工具箱提供的共进化分析方法包括SCA（统计耦联关联分析，Statistical Coupling Analysis)、ELSC（Explicit Likelihood of Subset Covariance)、MI（互信息，Mutual Information)、OMES（Observed Minus Expected Square)、McBASC（残基相关分析，McLanhlan? Based Substitution Correlation）以及DCA（直接关联分析，Direct-Coupling Analysis）。 -AK Toolbox is a Matlab Toolbox for co-evolution analysis for protein Multiple Sequence Alignment (MSA) distributed under Simplified BSD License.
The aim of AK toolbox is to provide a set of Matlab functions, which are independent of Matlab Bioinforamtics Toolbox, for coevolution anaylsis for MSA. At present, co-evolution methods available in this package are Statistical Coupling Analysis (SCA), Explicit Likelihood of Subset Covariance (ELSC), Mutual Information (MI), Observed Minus Expected Square method (OMES), McLanhlan Based Substitution Correlation (MCBASC), Direct-coupling analysis (DCA) and logR. Enclosed in this package is also an archive of different Bioinformatics matrices.
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Author: chengxin zhang |
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Description: 演化动力学中研究有限种群合作演化的遗传算法。源自哈佛大学nowak相关论文。-Co-evolution of the limited population dynamics study of the evolution of the genetic algorithm. Nowak related papers Harvard University.
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Author: yugi |
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Description: 将协同进化思想与微分进化算法结合，为一种改进的多种群共同进化微分进化算法-The idea of co-evolution differential evolution algorithm combined for an improved multi-population co-evolution differential evolution algorithm
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Author: 徐以坤 |
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Description: Due to the lack of local searching mechanism, the solution accuracy of kineticmolecular
theory optimization algorithm needs to be improved. And the misguidance
may occur when the current optimal values is a local extremum. In view of the
important role of elites in the optimization process, we improved the algorthm based
on co-evolution and elite strategy, and then proposed a M elite synergy kineticmolecular
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Author: yangs |
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Description: 本文提出变量随机分解策略，增加关联变量分配到同组的概率，使得算法更好的保留变量间的关联性，并将合作协同进化框架融合到算法中，提出了基于大规模变量分解的多目标粒子群优化算法-In this paper, a stochastic variable decomposition strategy is proposed to increase the probability of assigning related variables to the same group, which makes the algorithm better preserve the correlation between variables. The cooperative co-evolution framework is integrated into the algorithm, and a multi-objective decomposition based on large- Particle swarm optimization algorithm
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Author: 高雪树 |
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Description: 这是一个有效避免遗传算法早熟问题的函数优化方法，通过移民算子联系个种群，实现各种群的协同进化-This is an effective way to avoid the problem of premature genetic algorithm function optimization method, the sub-contact populations through migration operator, to achieve a variety of co-evolution group
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Author: 进哥哥 |
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Description: 标准型协同进化matlab代码实现 遗传算法的改进进化版本(Implementation of standard co-evolution matlab code)
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Author: 3737 |
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