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包含多目标遗传算法相关论文，对于深入了解NSGA及NAGA2算法有一定帮助-Multi-objective genetic algorithm that contains the relevant papers, for a deeper understanding and NSGA algorithms NAGA2 certainly be of help to
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多目标遗传算法工具箱，内容很详细，已经测试过，文件里面有怎么使用的说明-Micro Genetic Algorithm for Multiobjective Optimization，The code is distributed for academic purposes only. It has no warranty
implied or given, and the authors assume no liability for damage resulting from its u
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摘要:提出了一种新型的人工免疫算法用来解决多目标函数优化问题。基于自然免疫系统固有的优良特性对算法进行了设计和分析。
最后,算法对3个较复杂的多目标问题进行了优化,优化结果能很好地覆盖问题的Pareto最优面,并且把算法与某些混合遗传算法进行
了对比实验,表明人工免疫算法在解决多目标优化问题上具有可观的研究前景。
-Abstract:In order to effectively solve multiobjective optimization problems, a novel
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单目标、多目标遗传算法c++工具箱，Illinois Genetic Algorithms Laboratory-Single and Multiobjective Genetic Algorithm Toolbox in C++
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多目标问题的微遗传算法， ANSI C++ -Micro Genetic Algorithm for Multiobjective Optimization
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multiobjective Genetic algorithm with crowding distance
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多目标优化经典算法，性能排在前列，matlab开发环境-A popular non-domination based genetic algorithm for multiobjective optimization: NSGA-II
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A fast and elitist multiobjective genetic algorithm NSGA--A fast and elitist multiobjective genetic algorithm NSGA-II
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应用多目标遗传算法优化对PID参数进行多目标优化-Multiobjective genetic algorithm optimization of multi-objective optimization PID parameters
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基于非支配排序遗传算法处理多目标优化的matlab例程(A matlab routine for multiobjective optimization based on nondominated sorting genetic algorithm)
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chapter9 基于遗传算法的多目标优化算法(Chapter9 multi objective optimization algorithm based on genetic algorithm)
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是一个对多目标遗传算法进行改进的硕士论文，比较有价值。(It is a master's thesis to improve the multiobjective genetic algorithm, which is of great value.)
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MultiObjGA Code(多目标数值优化遗传算法)，教如何用matlab进行多目标遗传算法编程(MultiObjGA Code (multiobjective numerical optimization genetic algorithm), teaching how to use matlab for multiobjective genetic algorithm programming)
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利用多目标遗传算法，求得Pareto解集，为决断提供参考依据。(The multiobjective genetic algorithm is used to obtain the Pareto solution set, which provides a reference for the decision.)
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电力系统规划多目标遗传算法程序matlab程序(Multiobjective genetic algorithm)
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Multiple distributed generation units allocation in distribution network for
loss reduction based on a combination of analytical and genetic algorithm
methods
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