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sap-backend-master

于 2019-05-18 发布

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下载积分: 1 下载次数: 3

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代码说明：

说明：这个程序用来实现排课，利用的是·遗传算法，但这只是一个简单的程序(to deal with timetable scheduing)

文件列表：

sap-backend-master, 0 , 2017-09-25
sap-backend-master\.gitignore, 22 , 2017-09-25
sap-backend-master\GA.py, 10929 , 2017-09-25
sap-backend-master\README.rst, 35 , 2017-09-25
sap-backend-master\app.py, 8540 , 2017-09-25
sap-backend-master\courses.csv, 10094 , 2017-09-25
sap-backend-master\database.py, 4626 , 2017-09-25
sap-backend-master\database.pyc, 6394 , 2017-09-25
sap-backend-master\integrations, 0 , 2017-09-25
sap-backend-master\integrations\__init__.py, 199 , 2017-09-25
sap-backend-master\integrations\__init__.pyc, 414 , 2017-09-25
sap-backend-master\integrations\gclassroom.py, 4002 , 2017-09-25
sap-backend-master\integrations\gclassroom.pyc, 4340 , 2017-09-25
sap-backend-master\integrations\sap.py, 436 , 2017-09-25
sap-backend-master\integrations\sap.pyc, 1098 , 2017-09-25
sap-backend-master\models.pyc, 1514 , 2017-09-25
sap-backend-master\schema.pyc, 2566 , 2017-09-25
sap-backend-master\students.csv, 7598 , 2017-09-25
sap-backend-master\test.csv, 14392 , 2017-09-25

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一个关于adaboost和NN的matlab程序，对了解adaboost的过程和编写有很大的帮助。-this is a adaboost algorithm write with matlab code,and it is my starting program of researching work on adaboost,it is very helpful for application

2022-10-18 05:40:03下载

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2018-04-18 10:48:11下载

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2020-06-16 22:40:01下载

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SSMOPSO code
说明：形成策略被用来形成稳定的生态位，小生境/子种群被优化以并行地搜索和维护帕累托最优解。此外，还提出了一种自组织机制，以提高物种制定的效率和算法的性能。为了在决策空间和目标空间保持解决方案的多样性，SS-MOPSO采用了非主导排序方案和特殊拥挤距离技术。(In the proposed method, the speciation strategy is used to form stable niches and these niches/subpopulations are optimized to search and maintain Pareto-optimal solutions in parallel. Moreover, a self-organized mechanism is proposed to improve the efficiency of the species formulation as well as the performance of the algorithm. To maintain the diversity of the solutions in both the decision and objective spaces, SS-MOPSO is incorporated with the non-dominated sorting scheme and special crowding distance techniques. The performance of SS-MOPSO is compared with a number of the state-of-the-art multi-objective optimization algorithms on fourteen test problems. Moreover, the proposed SS-MOSPO is also employed to solve a real-life problem.)

2020-09-11 17:12:30下载

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USMC_Conventional_20190923
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2022-11-03 14:20:03下载

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2017-04-07 00:24:17下载

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