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【AI-NN-PR】 GA DL : 0: 遗传算法求解无约束目标函数，包含选择操作，变异操作，杂交操作，以及将十进制转化为二进制的编码操作-Genetic Algorithm unconstrained objective function comprises selecting operation, mutation, crossover operation, and will be converted to binary coded decimal operation
Update : 2024-05-18 Size : 3072 Publisher : Eric
【AI-NN-PR】 NSGA-II DL : 1: 多目标整数规划的遗传算法NSGA-IImatlab源代码，主程序、初始化、计算适应度、排序、选择、交叉变异、重组，最后得到Pareto前言。可以跑通，下载即用，具体方法介绍博客上文章上都有。-Multi-objective integer programming genetic algorithm NSGA-IImatlab source code, the main program, initialization, calculate fitness, sorting, selection, crossover and mutation, recombination, and finally get Pareto foreword. You can run through, downloading, using the specific method described has an article on the blog.
Update : 2024-05-18 Size : 12288 Publisher : hanhe
【matlab】 GA DL : 0: 遗传算法，fga.m 为遗传算法的主程序采用二进制Gray编码,采用基于轮盘赌法的非线性排名选择, 均匀交叉,变异操作,而且还引入了倒位操作!-GA, fga.m main genetic algorithm binary Gray code, based on the nonlinear method of ranking roulette selection, uniform crossover, mutation, and also introduced the inversion operation!
Update : 2024-05-18 Size : 4096 Publisher : 胡青
【matlab】 FDPSO DL : 0: PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。-Particle Swarm optimization
Update : 2024-05-18 Size : 5120 Publisher : 昔
【Other】 yichb DL : 0: 标准遗传算法，带解释，易理解。编码长度为10位，编码精度为0.0029。种群规模设为40，遗传算子分别为比例选择，单点交叉和单点变异。交叉概率0.7，变异概率0.1。-Standard genetic algorithm with explanation and easy to understand. Code length is 10 bits, the coding accuracy 0.0029. Population size is set to 40, respectively, the proportion of genetic operators selection, crossover and single-point single-point mutation. Crossover probability 0.7, mutation probability 0.1.
Update : 2024-05-18 Size : 1024 Publisher : 张
【matlab】 mat DL : 0: 在传统的K均值算法中引入了遗传算法和模拟退火算法,将两种算法相结合,通过交叉、变异、模拟退火等操作,实现了聚类分析。-The genetic algorithm and simulated annealing algorithm are introduced in the traditional K algorithm, and the two algorithms are combined to achieve the cluster analysis through the crossover, mutation, simulated annealing and so on.
Update : 2024-05-18 Size : 45056 Publisher : zhch78
【matlab】 matlab DL : 0: 用遗传算法实现函数最优化问题，程序源代码，包括适应度函数，选择，交叉，变异等函数文件。-Using genetic algorithm to realize the function optimization problem, the program source code, including the fitness function, selection, crossover, mutation function such as file
Update : 2024-05-18 Size : 6144 Publisher : yuanyuan
【matlab】 GA(PSO) DL : 0: 经典的遗传算法的实现，因为是框架因此可以直接套用，交叉概率、变异概率可以设成动态的-The realization of the classical genetic algorithm, because it is the framework can be directly applied, the probability of crossover probability, mutation probability can be set into a dynamic
Update : 2024-05-18 Size : 5120 Publisher : 李玥
【matlab】 TSP DL : 0: 根据混合粒子群算法原理，在MATLAB中编程实现基于粒子群算法的TSP搜索算法，给出了适应度函数，粒子初始化，交叉操作，变异操作，最后给出了仿真结果。有图可以看出，混合粒子群算法能够较快找到连接各个城市的最优路径，谢谢，希望能够给大家带来帮助。-According to the principle of hybrid particle swarm algorithm, programmed in MATLAB Based on Particle Swarm TSP search algorithm given fitness function, particle initialization, crossover and mutation operation. Finally, the simulation results. There can be seen, hybrid particle swarm algorithm can quickly find the optimal path to connect each city, thank you, hope to be able to give us help.
Update : 2024-05-18 Size : 12288 Publisher : wangxin
【matlab】 hanshuxunyou DL : 0: 根据遗传算法理论，在MATLAB软件中编程实现基本遗传算法寻找该函数最优解。遗传算法参数设置为：种群规模100，进化次数30，交叉概率为0.6，变异概率为0.1，并给出了基本遗传算法优化过程中各代平均函数值和最有个体函数值之间的变化图，希望对大家有帮助，-According to the theory of genetic algorithms in MATLAB software programming function of the genetic algorithm to find the optimal solution. Genetic algorithm parameters set as follows: population size 100, the evolution of the number 30, a crossover probability 0.6, mutation probability of 0.1, and gives the average value of the basic function of the genetic algorithm optimization process generations and the most change map function value between the individual , we want to help, thank you
Update : 2024-05-18 Size : 5120 Publisher : wangxin
【matlab】 pso DL : 0: 粒子群算法，通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优-Swarm optimization, through fitness to uate the quality of the solution, but it' s simpler than genetic algorithm rule, it does not have the genetic algorithm " cross" (Crossover) and " variation" (Mutation) operation, follow it through to the current search to find the optimal value of the global optimum
Update : 2024-05-18 Size : 1024 Publisher : 王哥
【matlab】 7057483zsyycsf DL : 0: 自适应遗传算法，对交叉算子和变异算子进行自适应运算-Adaptive genetic algorithm, crossover and mutation operator performs adaptive operation
Update : 2024-05-18 Size : 38912 Publisher : 廖长兵
【matlab】 mat DL : 0: 遗传算法MATLB程序，里面有遗传算法的选择、交叉、变异函数-GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function
Update : 2024-05-18 Size : 6144 Publisher : zhch92
【matlab】 liziqunsuanfa DL : 0: 粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO，是近年来由J. Kennedy和R. C. Eberhart等[1] 开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法。-Swarm optimization, also known as PSO (Particle Swarm Optimization), abbreviated as PSO, in recent years, one J. Kennedy and RC Eberhart et al. [1] developed a new evolutionary algorithm (Evolutionary Algorithm- EA). One of PSO algorithm and simulated annealing algorithm is similar to evolutionary algorithms, it is also a departure random solutions, through iterative find the optimal solution, it is also uated by the fitness of the solution quality, but it' s simpler than genetic algorithm rules it is no genetic algorithm " cross" (crossover) and " variation" (mutation) operation, follow it through to the current search to find the optimal value of the global optimum. This algorithm is its easy implementation, high precision, rapid convergence, etc. attracted academic attention, and demonstrated its superiority in solving practical problems. Particle swarm algorithm is a parallel algorithm.
Update : 2024-05-18 Size : 1024 Publisher : snowtiger
【matlab】 yichuansuanfa DL : 0: 用matlab实现遗传算法的代码。其中包括fitness、crossover等代码程序。-Using Matlab genetic algorithm to achieve the code. Including fitness, crossover and other code program.
Update : 2024-05-18 Size : 9216 Publisher : su
【matlab】 Adpga_optim DL : 0: 自适应遗传算法，有详细的编码，选择，交叉，变异等遗传操作的程序及说明。简单易懂-Adaptive genetic algorithm, a detailed coding, selection, crossover and mutation genetic manipulation procedures and instructions. easy to understand
Update : 2024-05-18 Size : 3072 Publisher : tx
【Other】 xujinpeng3.14 DL : 0: 求函数f x+10*sin(5*x)+7*cos(4*x)的最大值点，简单的单点交叉，基本位变异，赌轮盘选择，求的最好解是24.689. ga.m为主程序，运行其即可。参数可自己调。-The best solution is the maximum point of the function f x+ 10* sin (5* x)+ 7* cos (4* x), the simple single point crossover, the basic bit mutation, and the roulette wheel selection. . Ga.m as the main program, you can run it. Parameters can be adjusted.
Update : 2024-05-18 Size : 3072 Publisher : 王雁
【Linux-Unix】 PID DL : 0: 免疫粒子群算法用于PID整定。采用线性惯性系数，自适应交叉变异方法-Immune Particle Swarm Optimization for PID tuning. Linear inertia coefficient, adaptive crossover mutation method
Update : 2024-05-18 Size : 2048 Publisher : 武汉
【VHDL-FPGA-Verilog】 fp DL : 0: FPGA基础程序，分频器的设计及实现，利用计数器实现-FPGA based program, crossover design and implementation, realized by the counter
Update : 2024-05-18 Size : 358400 Publisher : 杜飞飞
【Software Engineering】 00245755 DL : 0: The paper discusses the simulation, performance and optimum design of single-phase-to- 2-phase cycloconvertors driving an induction motor. Bridge and centre-tapped circuits using triacs with double-integral control are considered. Results are presented for 2-pole induction-motor drives which may operate at speeds 0 to 1500 rev/min. The simulation is shown to model the performance of the drives successfully, and is used to predict an optimum design. An improvement to the control algorithm at bank crossover is presented. The bridge circuit is more efficient than the centre-tapped circuit, but uses more triacs.-The paper discusses the simulation, performance and optimum design of single-phase-to- 2-phase cycloconvertors driving an induction motor. Bridge and centre-tapped circuits using triacs with double-integral control are considered. Results are presented for 2-pole induction-motor drives which may operate at speeds 0 to 1500 rev/min. The simulation is shown to model the performance of the drives successfully, and is used to predict an optimum design. An improvement to the control algorithm at bank crossover is presented. The bridge circuit is more efficient than the centre-tapped circuit, but uses more triacs.
Update : 2024-05-18 Size : 584704 Publisher : behzad farhadi

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