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4下载：
印度Deb教授开发的Non-dominating Sorting Genetic Algorithm的改进版本，非常适合parato封面逼近运算。
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用C++语言写的非支配排序遗传算法的例子，顶！-Written in C++, the non-dominated sorting genetic algorithm for example, the top!
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Nondominated Sorting Genetic Algorithm
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nsga2非支配排序遗传算法，c++源码实现-nsga2 non-dominated sorting genetic algorithm, c++ source code to achieve
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non dominated sorting genetic algorithm second version
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Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization
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multi-objective genetic algorithm
non dominated sorting genetic algorithm
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该文件主要为分治策略、概率算法、回溯算法、枚举法、模拟退火算法、排序算法、搜索算法、贪心算法、遗传算法、蚁群算法的介绍及实现方式-The document mainly divide and conquer, probabilistic algorithms, backtracking algorithm, enumeration method, simulated annealing algorithm, sorting algorithm, search algorithm, greedy
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带等式约束的多目标遗传算法工具箱nondominated sorting genetic algorithmⅡ，NSGA-Ⅱ优化源代码-With equality constraint multi-objective genetic algorithm toolbox nondominated sorting genetic algorithm Ⅱ NSGA-II optimized source code
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多目标的经典遗传算法MOEA-NSGA-II，采用平衡集与非支配排序方法,Classic multi-objective genetic algorithm MOEA-NSGA-II, using the non-dominated sorting method
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MATLAB Code for: Non-dominated Sorting Genetic Algorithm II (NSGA-II)
Version 1.1 - November 2011 Programmed By:
S. Mostapha Kalami Heris (MatlabSite Member)-MATLAB Code for: Non-dominated Sorting Genetic Algorithm II (NSGA-II)
Version 1.1
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基于遗传算法的作业排序方法
利用梯度下降法求解线性判别函数-Job sorting method based on genetic algorithm using the gradient descent method for solving linear discriminant function
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Parsopoulos等采用罚函数法，利用非固定多段映射函数对约束优化问题进行转化，再利用PSO算法求解转化后问题，仿真结果显示PSO算法相对遗传算法更具有优越性，但其罚函数的设计过于复杂，不利于求解；Hu等采用可行解保留政策处理约束，即一方面更新存储中所有粒子时仅保留可行解，另一方面在初始化阶段所有粒子均从可行解空间取值，然而初始可行解空间对于许多问题是很难确定的；Ray等提出了具有多层信息共享策略的粒子群原理来处理约束，根据约束矩阵采用多层Pareto排序机制来产生优良粒子，进而用一些优良的
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matlab环境下实现的非支配排序遗传算法（NSGA-II），该算法在快速找到Pareto前沿和保持种群多样性方面都有很好的效果-matlab environment to achieve non-dominated Sorting Genetic Algorithm (NSGA-II), the algorithm quickly find the Pareto frontier and maintaining the diversity of the population has a goo
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Non-dominated Sorting Genetic Algorithm II (NSGA-II)
Version 1.1 - November 2011 Non-domin ated Sorting Genetic Algorithm II (NSGA-II)
Version 1.1 - November 2011 Non-domin ated Sorting Genetic Algorithm II (NSGA-II)
Version 1.1 - Novemb
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教与学算法（TLBO），是一种非常好用人工智能算法，类似于遗传算法，可以用在算法寻优和调度排序等方面。-Teaching and learning algorithms (TLBO), is a very easy to use artificial intelligence algorithms, like genetic algorithm, the algorithm can be used in the optimization and scheduling sorting and so
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一种快速多目标遗传算法，NSGA-Ⅱ是目前最流行的多目标进化算法之一，它降低了非劣排序遗传算法的复杂性，具有运行速度快，解集的收敛性好的优点，成为其他多目标优化算法性能的基准。-A fast multi-objective genetic algorithm, NSGA-Ⅱ is the most popular one multi-objective evolutionary algorithm, which reduces the non-dominated sorting genetic
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NSGAII(Non-Dominated Sorting in Genetic Algorithms) 进化算法，用于解决多目标问题的最经典算法，-a popular nondomination based genetic algorithm for multi-objective optimization
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工业中，用于换热网络优化的非支配排序遗传算法源程序-In industry, the non dominated sorting genetic algorithm is used to the optimization of the heat exchanger network
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Non Dominated Sorting Genetic Algorithm 2
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