Description: 基于自适应遗传算法的电液弯辊模糊控制系统 很不错的文章 对自适应遗传算法模糊控制进行的详细的分析-Based on adaptive genetic algorithm fuzzy bending electro-hydraulic control system is very good article on adaptive genetic algorithm for fuzzy control of a detailed analysis of Platform: |
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Author:李博 |
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Description: 自适应遗传算法的MATLAB程序，自己编写的。参照了一些高手的代码，有些自己的特色-Adaptive Genetic Algorithm MATLAB procedures, I have written. Reference to a number of master code, some of its own characteristics Platform: |
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Author:sunbeam |
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Description: 用C语言程序实现遗传算法的功能，遗传算法是模拟自然界生物进化过程与机制求解极值问题的一类自组织、自适应的人工智能技术，它模拟达尔文的自然进化论与孟德尔的遗传变异理论，具有并行性等特点，简单通用。-C language program used to achieve the function of genetic algorithm, genetic algorithm is a simulation of natural biological evolution process and mechanism for solving the problem for a class of extremal self-organizing, adaptive artificial intelligence technology, which simulated the natural Darwinian theory of evolution with Mendelian theory of genetic variation with features such as parallelism, simple common. Platform: |
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Author:刘丽 |
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Description: 自适应遗传算法MATLAB源代码，内含多个子文件，测试时运行mypath文件即可-Adaptive genetic algorithm MATLAB source code that contains multiple sub-documents, test documents can be run mypath Platform: |
Size: 2048 |
Author:meng |
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Description: 一个较好的自适应遗传算法，求解速度快，可移植性好，图形显示，并有例子可供操作。-A better self-adaptive genetic algorithm for solving speed, portability, and graphical display, and have examples for the operation. Platform: |
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Author:赵辉 |
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Description: 这是自适应遗传算法的MATALB代码,经调试运行通过,希望对大家有用-This is self-adaptive genetic algorithm MATALB code, adopted by the debugger to run, everyone would like to be useful Platform: |
Size: 5120 |
Author:liyan |
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Description: 本算法为自适应遗传算法，采用基于排序的选择策略，编码方案为实数编码。能够解决很多优化问题！-The algorithm for the adaptive genetic algorithm, based on the sort of choice strategy, coding schemes for real-coded. Many optimization problems can be solved! Platform: |
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Author:邱腾飞 |
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Description: 本程序是用自适应遗传算法进行图像增强，其思想来自《计算机学报》发表的论文。-This program is self-adaptive genetic algorithm image enhancement, their thoughts from the " Journal of Computers," published papers. Platform: |
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Author:Bruce |
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Description: 用基本遗传算法求解一维无约束优化问题
用顺序选择遗传算法求解一维无约束优化问题
用动态线性标定适应值的遗传算法求解一维无约束优化问题
用大变异遗传算法求解一维无约束优化问题
用自适应遗传算法求解一维无约束优化问题
用双切点遗传优化求解一维无约束优化问题
用多变异位自适应遗传优化求解一维无约束优化问题
-The basic genetic algorithm with one-dimensional sequence of unconstrained optimization problems using genetic algorithm selected one-dimensional non-linear constrained optimization problem of dynamic calibration of genetic algorithm to adapt to the value of one-dimensional unconstrained optimization genetic algorithm with a large variation of one-dimensional unconstrained adaptive genetic algorithm for optimization problem solving unconstrained optimization problems one-dimensional cut by two-dimensional point of GA for solving a problem of unconstrained optimization adaptive genetic optimization with variable ectopic solving one-dimensional unconstrained optimization problem Platform: |
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Author:胖子 |
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Description: 自适应GA SVM 参数选择算法研究Param eter selection algorithm for support vector machines based
on adaptive genetic algorithm
支持向量机是一种非常有前景的学习机器, 它的回归算法已经成功地用于解决非线性函数的逼近问题. 但
是, SVM 参数的选择大多数是凭经验选取, 这种方法依赖于使用者的水平, 这样不仅不能获得最佳的函数逼近效果,
而且采用人工的方法选择 SVM 参数比较浪费时间, 这在很大程度上限制了它的应用. 为了能够自动地获得最佳的
SVM 参数, 提出了基于自适应遗传算法的 SVM 参数选取方法. 该方法根据适应度值自动调整交叉概率和变异概率,
减少了遗传算法的收敛时间并且提高了遗传算法的精度, 从而确保了 SVM 参数选择的准确性. 将该方法应用于船用
锅炉汽包水位系统建模, 仿真结果表明由该方法所得的 SVM 具有较简单的结构和较好的泛化能力, 仿真精度高, 具
有一定的理论推广意义.- The support vector m achine ( SVM ) is a prom ising artificia l inte lligence technique, in w hich the regres
sion a lgorithm has a lready been used to so lve the nonlinear function approach successfully. Un fortunate ly, m ost us
ers se lect param eters for an SVM by ru le o f thumb, so they frequently fail to generate the optim al approach ing e ffect
for the function. Th is has restricted effective use o f SVM to a great degree. In order to get optim a l param eters auto
m atically, a new approach based on an adaptive genetic a lgorithm ( AGA ) is presented, w hich autom atica lly ad
justs the param eters for SVM. This m ethod se lects crossover probability and mutation probab ility accord ing to the
fitness va lues of the object function, therefo re reduces the convergence tim e and im proves the prec ision o fGA, in
suring the accuracy of param eter se lection. Th is m ethod w as applied to m odeling of w ater level system o f a sh ip
b Platform: |
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