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用于求解约束优化问题的算法，算法为差分进化/遗传算法/微粒群算法的融合。对于“[7] T. P. Runarsson and X. Yao, Stochastic ranking for constrained evolutionary optimization, IEEE Trans. Evol. Comput., vol. 4, no. 3, pp. 284-294, Sep. 2000”中给出的13个标准测试函数，均能得到问题最优解。如有任何疑问，请于http://2shi.phphube
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遗传算法是一种模拟生物进化机制的随机全局优化搜索方法，具有很强的全局优化能力及鲁棒性。遗传算法属于直接搜索法，对适应函数基本无限制，既不要求连续，也不要求函数可微，而且不需要初始信息可以寻求全局最优解克服了单纯形算法初始条件影响大，易陷入局部最小等缺点，操作方便，速度快，不需要复杂的规则，且可用于多目标寻优，在解空间进行高效启发式搜索，可以提高运算速度。-The genetic algorithm is one simulation organic evolution mechanism sto
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用遗传算法求解随机模型的C++实现，绝对能够运行成功的-Genetic algorithm with stochastic models of the C++ to achieve, definitely be able to run successful
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A novel parallel quantum genetic algorithm for stochastic job
shop scheduling
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JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.-JavaGenes is an evolutionary software system written in Java. It implements
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This a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those foun
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Global Optimization Toolbox provides methods that search for global solutions to problems that contain multiple
maxima or minima. It includes global search, multistart, pattern search, genetic algorithm, and simulated
annealing solvers. You can u
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遗传算法是一类随机优化算法，但它不是简单的随机比较搜索，而是通过对染色体的评价和对染色体中基因的作用，有效地利用已有信息来指导搜索有希望改善优化质量的状态。-Genetic algorithms are a class of stochastic optimization algorithm, but it is not a simple comparison of random search, but through the evaluation of chromosomes and gene
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基于遗传算法的随机优化搜索 ppt 讲解-genetic algorithm based stochastic optimization search
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遗传算法的描述。遗传算法是一种很经典的随机化过程的算法，在实际应用中应用广泛，效果良好。-A descr iption of the genetic algorithm. The genetic algorithm is a classical stochastic process of the algorithm, widely used in practical applications to good effect.
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该算法是用随机模拟，遗传算法和神经网络相结合的混合算法求解随机期望模型的。-The algorithm is simulated using a stochastic, combining genetic algorithm and neural network hybrid algorithm for solving stochastic expected value model.
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该算法是用随机模拟，遗传算法和神经网络相结合的混合算法求解随机机会约束的模型。-The algorithm is simulated using a stochastic, combining genetic algorithm and neural network hybrid algorithm for solving stochastic chance constrained model.
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二阶段随机规划问题基于随机模拟的遗传算法 用于解决随机模拟问题-The two-stage stochastic programming stochastic simulation-based genetic algorithm for solving stochastic simulation
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随机共振过程中，针对参数a和b的优化处理，此处使用的是遗传算法。-Stochastic resonance process, the parameters a and b for the optimization, genetic algorithm is used here.
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求解非线性方程组方法有经典算法以及近年来流行的遗传算法．牛顿法及其改进形式,但是此类算法的收敛性在很大程度上依赖于初始点的选择，对于某些非线性方程组容易导致求解失败 为了克服经典算法的缺点，设计了求解非线性方程组的混合遗传算法,但依然对方程组和编码方法有很高要求。PSO是受到鸟群或者鱼群社会行为的启发而形成的一种基于种群的随机优化技术。它是一类随机全局优化技术，通过粒子间的相互作用发现复杂搜索空间中的最优区域。该算法是一种基于群体智能的新型演化计算技术，具有简单易实现、设置参数少、全局优化能力强
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System reliability analysis and optimization are important to efficiently utilize available resources and to develop an optimal system design architecture. System reliability optimization has been solved by using optimization techniques including met
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遗传算法（Genetic Algorithm，GA）是通过对自然界中生物的遗传和优胜劣汰的进化过程进行模拟与抽象，进而形成的一种自适应全局随机优化搜索方法。遗传算法只需提供目标函数作为寻优信息，它从某一随机生成的初始群体出发，经过选择、交叉和变异等遗传操作后对个体进行适应度评价，保留适应度较强的个体遗传到子代种群中，经过多次的迭代计算求得最优个体，即问题的最优解。本程序采用遗传算法可求解微网优化运行。-Genetic Algorithm is an adaptive global by natu
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matlab环境下遗传算法（GA）同时优化随机共振（SR）参数a、b源程序-The source code of optimization of stochastic resonance parameters using genetic algorithm under the environment of MATLAB
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Genetic Algorithm for 1D TDEM Datasets stochastic inversion
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The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individ
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