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Description: 遗传算法MATLB程序，里面有遗传算法的选择、交叉、变异函数，一些简单的MABTLAB遗传算法例子！-GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function, some simple examples MABTLAB GA!
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Author: enao |
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Description: matlab程序 遗传算法变异程序 敬请各位高手指教-Matlab procedures genetic algorithm variation procedures you please enlighten master
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Author: 闫小月 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).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。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 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) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross - (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications
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Size: 16633 |
Author: 张正 |
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Description: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation).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。系统初始化为一组随机解，通过叠代搜寻最优值。但是并没有遗传算法用的交叉(crossover)以及变异(mutation)。而是粒子在解空间追随最优的粒子进行搜索。详细的步骤以后的章节介绍 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) is an evolutionary technology (evolutionary computation). Predatory birds originated from the research PSO with similar genetic algorithm is based on iterative optimization tools. Initialize the system for a group of random solutions, through iterative search for the optimal values. However, there is no genetic algorithm with the cross- (crossover) and the variation (mutation). But particles in the solution space following the optimal particle search. The steps detailed chapter on the future of genetic algorithm, the advantages of PSO is simple and easy to achieve without many parameters need to be adjusted. Now it has been widely used function optimization, neural networks, fuzzy systems control and other genetic algorithm applications
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Size: 16384 |
Author: |
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Description: 遗传算法MATLB程序，里面有遗传算法的选择、交叉、变异函数，一些简单的MABTLAB遗传算法例子！-GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function, some simple examples MABTLAB GA!
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Size: 6144 |
Author: enao |
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Description: matlab程序 遗传算法变异程序 敬请各位高手指教-Matlab procedures genetic algorithm variation procedures you please enlighten master
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Author: 闫小月 |
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Description: 提供一个人工免疫算法源程序，其算法过程包括：
1.设置各参数
2.随机产生初始群体——pop=initpop(popsize,chromlength)
3.故障类型编码，每一行为一种！code(1,:)，正常；code(2,:)，50％；code(3,:)，150％。实际故障测得数据编码，这里Unnoralcode,188％
4.开始迭代（M次）：
1）计算目标函数值：欧氏距离[objvalue]=calobjvalue(pop,i)
2）计算群体中每个个体的适应度fitvalue=calfitvalue(objvalue)
3）选择newpop=selection(pop,fitvalue) objvalue=calobjvalue(newpop,i) ％
交叉newpop=crossover(newpop,pc,k) objvalue=calobjvalue(newpop,i) ％
变异newpop=mutation(newpop,pm) objvalue=calobjvalue(newpop,i) ％
5.求出群体中适应值最大的个体及其适应值
6.迭代停止判断。-provide a source of artificial immune algorithm, the algorithm process include : 1. Two of the parameters set. Initial randomly generated groups-- pop = initpop (popsize, chromlength) 3. Fault type coding, each act a! Code (1 :), normal; Code (2, :), 50%; Code (3 :), 150%. Fault actual measured data coding, here Unnoralcode, 188% 4. Beginning iteration (M) : 1) the objective function value : Euclidean distance [objvalue] = calobjvalue (pop, i) 2) calculation of each individual groups of fitness calfitvalue fitvalue = ( objvalue) 3) = newpop choice selection (pop, fitvalue) objvalue = calobjvalue (newpop, i) =% newpop cross-crossover (newpop, pc, k) = calobjvalue objvalue (newpop, i) =% variation newpop mutation (newpop, pm ) objvalue = calobjvalue (newpop, i)% 5. groups sought to adapt th
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Author: 江泉 |
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Description: 小波变换极大值推算突变点的奇异度指标-wavelet transform maximum projection of singular point mutation indicator
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Size: 8192 |
Author: wwhblue |
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Description: ADPTIVE GA是改进遗传算法程序,提供了各种交叉算子,变异算子,具有强大的计算功能-ADPTIVE GA is improved genetic algorithms and procedures, and providing a variety of cross-operator, mutation operator, with powerful computing capabilities
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Author: 周期函数 |
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Description: ％ [BestPop,Trace]=fmaxga(FUN,LB,UB,eranum,popsize,pcross,pmutation)
％ Finds a maximum of a function of several variables.
％ fmaxga solves problems of the form:
％ max F(X) subject to: LB <= X <= UB
％ BestPop--------最优的群体即为最优的染色体群
％ Trace----------最佳染色体所对应的目标函数值
％ FUN------------目标函数
％ LB-------------自变量下限
％ UB-------------自变量上限
％ eranum---------种群的代数,取100--1000(默认1000)
％ popsize--------每一代种群的规模；此可取50--100(默认50)
％ pcross---------交叉的概率,此概率一般取0.5--0.85之间较好(默认0.8)
％ pmutation------变异的概率,该概率一般取0.05-0.2左右较好(默认0.1)
％ options--------1×2矩阵,options(1)=0二进制编码(默认0),option(1)~=0十进制编码,option(2)设定求解精度(默认1e-4)- [BestPop, Trace] = fmaxga (FUN, LB, UB, eranum, popsize, pcross, pmutation) Finds a maximum of a function of several variables. Fmaxga solves problems of the form: max F (X) subject to : LB <= X <= UB BestPop-------- optimal chromosome groups is the best group Trace---------- chromosome corresponding to the best objective function value FUN------------ objective function LB------------- variable lower limit since the UB------------- variable upper limit eranum--------- populations algebra, take 100- 1000 (default 1000) popsize-------- population size of each generation this desirable 50- 100 (default 50) pcross--------- crossover probability, the probability of a general check 0.5- 0.85 between the better (default 0.8) pmutation------ mutation probability, the probability of 0.05 general admission better about-0.2 (default 0.1) options-------- 1 × 2 matrix, options (1) = 0 binary code (default 0), option (1) ~ = 0 decimal coding, option (2 ) set accuracy (default 1e-4)
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Size: 3072 |
Author: mmcc |
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Description: maltab遗传算法源程序，此程序为一个一个的小程序分开的，很完整。包括编码，设定初始种群，交叉，变异，及结束条件等-maltab genetic algorithm source code, this procedure a small as a separate process, it is complete. Including encoding, set the initial population, crossover and mutation, and the end conditions
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Size: 7168 |
Author: joean |
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Description: matlab 的一些实例，希望对大家有所帮助，多多参考-reference no no no no no no no no no
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Size: 1024 |
Author: 高海龙 |
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Description: 介绍遗传算法的原理，流程。详细展示了交叉，变异，选择等算子。同时，还介绍了谢菲尔德大学遗传工具箱的使用 。本书对初学遗传算法者很有帮助。-Introduce the principle of genetic algorithms, processes. Detail shows crossover, mutation, selection operator. Meanwhile, the University of Sheffield also introduced the use of genetic toolbox. Genetic algorithms for beginners who book very helpful.
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Size: 9717760 |
Author: CaoJunlong |
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Description: 遗传算法MATLB程序，里面有遗传算法的选择、交叉、变异函数，一些简单的MABTLAB遗传算法例子！(GA MATLB procedures, there are genetic algorithm selection, crossover and mutation function, some simple examples MABTLAB GA!)
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Size: 89088 |
Author: Cookie_upc
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Description: 遗传算法的基本步骤如下：
1）在一定编码方案下，随机产生一个初始种群；
2）用相应的解码方法，将编码后的个体转换成问
题空间的决策变量，并求得个体的适应值；
3）按照个体适应值的大小，从种群中选出适应值
较大的一些个体构成交配池；
4）由交叉和变异这两个遗传算子对交配池中的
个体进行操作，并形成新一代的种群；
5）反复执行步骤2-4，直至满足收敛判据为止。(The basic steps of the genetic algorithm are as follows:
1) under certain coding schemes, an initial population is randomly generated;
2) use the corresponding decoding method to convert the encoded individuals into questions
The decision variable of the problem space is obtained and the fitness value of the individual is obtained;
3) according to the size of individual fitness, the fitness is selected from the population
Larger individuals constitute mating pools;
4) by crossover and mutation, these two genetic operators are pairs of mating pools
Individuals operate and form a new generation of populations;
5) repeat step 2-4 until the convergence criterion is satisfied.)
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Author: 傲视天下
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Description: use crossover, mutation, selection
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Size: 10240 |
Author: cicily
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Description: 利用matlab实现变异，交叉，选择等功能(crossover, mutation with MATLAB)
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Size: 10240 |
Author: cicicily
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Description: 利用matlab实现种群的选择，交叉，变异等功能(realize of selection, mutation and crossover)
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Size: 10240 |
Author: cicicicil
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Description: Matlab 遗传算法(Genetic Algorithm)优化工具箱是基于基本操作及终止条件、二进制和十进制相互转换等操作的综合函数库。其实现步骤包括：通过输入及输出函数求出遗传算法主函数、初始种群的生成函数，采用选择、交叉、变异操作求得基本遗传操作函数。以函数仿真为例，对该函数优化和GA 改进，只需改写函数m 文件形式即可。(The Matlab Genetic Algorithm optimization toolbox is a comprehensive function library based on basic operations and termination conditions, binary and decimal conversion and other operations. The implementation steps include: the main function of genetic algorithm and the generation function of the initial population are obtained by the input and output functions, and the basic genetic operation function is obtained by the selection, crossover and mutation operations. Taking function simulation as an example, the function optimization and GA improvement only need to rewrite function m file form)
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Size: 9216 |
Author: FZenjoys |
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Description: 免疫算法求解配送中心选址问题，配送中心向需求点配送货物是供应链中的重要部分.本文以成本最低为目标函数,把距离上限加入到惩罚机制,并根据抗体和抗原之间的亲和力设计自适应交叉和变异概率,把自适应的免疫算法应用到配送中心模型中进行求解,最后通过仿真实验对比验证了算法用在配送中心选址上有较好的效果.(Immune Algorithm is used to solve the location problem of Distribution Center, which is an important part of supply chain. This paper takes the lowest cost as the objective function, adds the upper distance limit to the penalty mechanism, and designs the adaptive crossover and mutation probability according to the affinity between antibody and Antigen, the adaptive immune algorithm is applied to the distribution center model to solve the problem. Finally, the simulation results show that the algorithm is effective in the location of Distribution Center.)
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Author: 代码大小姐 |
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