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【Other resource】 algorithm_GP DL : 0: 演化算法的示例代码,包括演化算法的全部实现过程,主要操作包括选择,交叉,变异-evolutionary algorithm code examples, including the evolution of the algorithm to achieve full process, including the operation of the main selection, crossover, Variation
Update : 2008-10-13 Size : 12667 Publisher : cai
【Mathimatics-Numerical algorithms】 yichuan DL : 0: 遗传算法基本算法，遗传算法自适应算法，遗传算法交叉算法-GA basic algorithm, genetic algorithm adaptive algorithm, genetic algorithm crossover algorithm
Update : 2008-10-13 Size : 20033 Publisher : zxq
【Other resource】 Ga DL : 0: 用c语言编写的matlab遗传算法程序，包含：select,crossover,mutator等。非常适合初学者学习，程序比较清晰，而且不是很复杂。
Update : 2008-10-13 Size : 79816 Publisher : fly
【Other resource】 PSO-evolutionarycomputation DL : 1: 粒子群优化算法(PSO)是一种进化计算技术(evolutionary computation)，有Eberhart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。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
Update : 2008-10-13 Size : 22694 Publisher : zzh
【Other resource】 SGALABbugfix DL : 3: 多目标遗传算法程序 to run Demo files, is to run SGALAB_demo_*.m what s new: 1) Multiple-Objective GAs VEGA NSGA NPGA MOGA 2) More TSP mutation and Crossover methods PMX OX CX EAX Boolmatrix 3) More selection methods Truncation tornament stochastic 4) mutation methods binary single point int/real single point 5) encoding/decoding methods binary integer/real messy gray DNA permuation to fix the plot bugs for 4001 , download this file and replace old files.
Update : 2008-10-13 Size : 80294 Publisher : 馨竹
【Linux-Unix】 wine-doors-0.1.2 DL : 0: wine-doors 是基于wine的GNOME桌面软件。 wine-doors是一个软件安装管理包（类似新立得），用户可以选择要安装的软件。wine-doors可以在linux,Solaris和Unix 下运行。wine-doors将计划开发支持Cedega, cvscedega and Crossover Office的版本。
Update : 2008-10-13 Size : 283062 Publisher : elvis
【Other resource】遗传算法的三个算子 DL : 0: 改进的遗传算法的三个操作算子，包括选择、交叉和变异。-Improved three arithmetic operator in genetic algorithm including select,crossover and mutuation
Update : 2008-10-13 Size : 2153 Publisher : 胡玉霞
【Other resource】基因计算 DL : 0: 简单遗传算法(SGA) 主要算法模块有:选择交叉变异 (三个遗传操作) 和群体更新 -simple genetic algorithm (SGA) algorithm for the main modules are : choice of crossover and mutation (3 Genetic Manipulation) and update groups
Update : 2008-10-13 Size : 4082 Publisher : 赵小美
【Other resource】差别算法matlab源码 DL : 0: 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
Update : 2008-10-13 Size : 16633 Publisher : 张正
【Mathimatics-Numerical algorithms】遗传算法SGA DL : 0: 包含一个cpp文件,含有main()函数! 主要函数如下 void InitData(); int Flip(float probabiliby); float Random(); //generate a pseudorandom integer from 0 to 1 void ResetRandom(); //reset pseudorandom integer array fRand float ObjFunc(float vx); float DeCode(unsigned * pChrom); void StatPop(POP * pop); void InitPop(); //initialize population void InitReport(); //initial info report unsigned Select(); int Mutation(unsigned chromval); int CrossOver(unsigned * parent1,unsigned * parent2,int popidx); void UpdateGen(); void Report(int gen);
Update : 2009-05-23 Size : 537650 Publisher : arkzhu
【Console】 tsp.cpp DL : 0: 用遗传算法（Genetic algorithm）解决Travel salesperson problem. Crossover类型：one-point和two-point. 选择类型：Tournament和RouletteWheel.
Update : 2011-04-29 Size : 2844 Publisher : lightlid
【AI-NN-PR】 A Comparison of Crossover and Mutation in Genetic DL : 0: 遗传算法的文章- Heredity algorithm article
Update : 2024-05-04 Size : 917504 Publisher : 顾凡一
【AI-NN-PR】基因计算 DL : 0: 简单遗传算法(SGA) 主要算法模块有:选择交叉变异 (三个遗传操作) 和群体更新 -simple genetic algorithm (SGA) algorithm for the main modules are : choice of crossover and mutation (3 Genetic Manipulation) and update groups
Update : 2024-05-04 Size : 4096 Publisher : 赵小美
【AI-NN-PR】差别算法matlab源码 DL : 0: 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
Update : 2024-05-04 Size : 16384 Publisher :
【AI-NN-PR】 binary DL : 0: 利用遗传算法求最小值，程序中求得是表达式x1*x1+x2*x2+x3*x3再-2~2上的最小值，以及对应的x值，算法中使用二进制编码，交叉采用不同交叉和优势交叉，变异也分两种，不用变异和优势变异-using genetic algorithms for the minimum, procedures to seek expressions x1 x1 x2*** x2 x3 x3 another 2 ~ 2 on the minimum, and the corresponding value of x, algorithm using binary encoding, using different cross-cross and advantages of crossover and mutation at the two without variation and variation advantage
Update : 2024-05-04 Size : 11264 Publisher : 陈仕林
【AI-NN-PR】特征提取 DL : 1: 特征提取：重点是几何特征（环，交叉点，端点）和变换,例子就是这些几何特征的提取。-Feature Extraction : focus on the geometric characteristics (Central, a crossover point, endpoint) and the transformation of these examples is the geometric features are extracted.
Update : 2024-05-04 Size : 53248 Publisher : 何风
【matlab】 matlab遗传算法程序(new) 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，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!
Update : 2024-05-04 Size : 6144 Publisher : enao
【matlab】 mutation DL : 0: 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
Update : 2024-05-04 Size : 1024 Publisher : 闫小月
【Other】 VC_GAD DL : 0: 简单遗传算法VC实现，包括选择，交叉，变异以及种群初始化-simple genetic algorithm VC, including the selection, crossover and mutation, and initialization Stocks
Update : 2024-05-04 Size : 208896 Publisher : 王斌
【AI-NN-PR】 immunity DL : 0: 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
Update : 2024-05-04 Size : 9216 Publisher : 江泉

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