Search - crossover matlab

Search - crossover matlab - List

[Other resource] 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!
Date : 2008-10-13 Size : 6.71kb User : enao
[Other resource] crossover DL : 0: matlab环境下的遗传算法交叉程序，有不当之处敬请指教-Matlab environment of cross-GA procedures, it can be faulted Comments enlighten
Date : 2008-10-13 Size : 880byte User : 闫小月
[Other resource] immune-genetic-algorithm DL : 0: 免疫遗传算法matlab 程序,该算法由抗原识别、初始抗体产生、适应度计算、向记忆细胞分化、抗体的促进和抑制、抗体产生(交叉、变异) 六个模块组成-immune genetic algorithm Matlab procedure, the algorithm by antigen identification, initial antibody, fitness, to the memory cells, antibody for the promotion and inhibition, antibody (crossover and mutation) six modules
Date : 2008-10-13 Size : 1.68kb User : He Hong
[Other resource] AGAandLAGA DL : 0: 两种改进的遗传算法（自适应交叉概率的遗传算法，加入领域竞争策略的遗传算法）相比较的matlab程序，基于UCI的两个数据集，可直接运行程序观察效果。-both improved genetic algorithm (adaptive crossover probability of genetic algorithm, to field a competitive strategy of genetic algorithm) compared to the Matlab procedures, the UCI based on two data sets, can run the program directly observe the effect.
Date : 2008-10-13 Size : 370.61kb User : zym
[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。
Date : 2008-10-13 Size : 77.95kb User : fly
[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
Date : 2008-10-13 Size : 16.24kb User : 张正