搜索资源列表
MOEA-NSGA-II
- 基于进化算法的多目标优化,以matlab程式所寫成,請大家參考參考-evolutionary algorithm based on the multi-objective optimization to written Matlab program, please refer to reference
NSGA-II多目标规划代码（matlab版）
- 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。
NSGA-II.rar
- 优化算法matlab代码
GA
- 由我收集或写出的GA源码，包括GA、SGA、AGA、TSPGA、GAPID、NSGA、NSGA2等。-From my collection or write source of the GA, including the GA, SGA, AGA, TSPGA, GAPID, NSGA, NSGA2 and so on.
MOGA-SVM
- Moga- SVM procedures contain three documents : 1.moga 2.nsga
MOEA-NSGA-II
- genetic algorithm, simulated annealing, singleobjective, particle swarm optimi..., optimization, classes
MOEA-NSGA-II
- genetic algorithm program for power system
NSGA-II
- nsga2是解决多目标问题的经典算法，文件中为nsga2的matlab的源代码-nsga2 is designed for multiobjective problems
NSGA-II
- 这是一个用matlab编写的NSGA2标准程序，对初学多目标算法的人很有帮助。-This is a the NSGA2 standard procedures beginner multi-objective algorithm using matlab helpful.
NSGA2 matlab
- this is a matlab code for a NSGA-II algorithm
nsga22
- % The NSGA-II multi-objective genetic algorithm w/ % simulated binary crossover (SBX) and polynomial mutation. %
MOEA-NSGA-II
- matlab multiobjective
MATLAB-Codes
- 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
NSGA-II
- 利用matlab编写的遗传算法程序，很经典，是从一个毕业的师姐那要过来的，跟大家分享一下-Prepared using matlab genetic algorithm, very classic, is a graduate of the senior sister apprentice that to come and share with you
nsga-ii-binary
- 非支配排序的多目标优化算法，二进制编码，MATLAB 平台-Multi-objective optimization of non-dominated sorting algorithm, binary coding, MATLAB platform
NSGA-II(matlab程序)
- 基于多目标优化的遗传算法，非常适合parato逼近运算
NSGA-II
- 拉丁超立方采样matlab程序：在尽可能少的样本点下建立尽可能精确的模型-latin hypercube sample
01-NSGA-II
- 一个多目标优化遗传算法matlab源代码-a nsga2 matlab code
NSGA-II
- 非支配排序遗传算法MATLAB代码实例，用于非支配排序遗传算法优化-Non-dominated Sorting Genetic Algorithm MATLAB code examples for non-dominated sorting genetic algorithm optimization
NSGA
- 多目标遗传算法是NSGA-II[1]（改进的非支配排序算法）,该遗传算法相比于其它的多目标遗传算法有如下优点：传统的非支配排序算法的复杂度为 ，而NSGA-II的复杂度为 ，其中M为目标函数的个数，N为种群中的个体数。引进精英策略，保证某些优良的种群个体在进化过程中不会被丢弃，从而提高了优化结果的精度。采用拥挤度和拥挤度比较算子，不但克服了NSGA中需要人为指定共享参数的缺陷，而且将其作为种群中个体间的比较标准，使得准Pareto域中的个体能均匀地扩展到整个Pareto域，保证了种群的多样性