掌握编程过程中遇到的思路方面和技术方面的问题. 内容包括以下几个方面:
反向逻辑思维能力的培养;
建立清晰的编程逻辑思维能力;
GH 的程序设计理念;
并行数据结构深入理解和控制.
Grasshopper course of McNeel Asia focus on the cultivation of students flexible use of programming techniques, the ability to solve practical problems. Our course deep into the whole process of programming, from programming thinking model, the components principle to usage details do detailed explanation, help students complete mastery programming encountered in the process of thinking and technical aspects, include the following content:
Ability of reverse logical thinking;
Establishment of clear programming logical thinking ability;
The program design concept of Grasshopper;
Understanding parallel data tree structure and how to control it.
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授课讲师 Instructor 课程由Grasshopper原厂McNeel公司在中国地区的两位 Rhino 原厂技术推广工程师 – Dixon、Jessesn联合授课。课程结束后对达到授课预定目标的学员颁发唯一由Grasshopper原厂认证的结业证书.
Dixon & Jessesn, McNeel Asia Support engineer, by the end of course student who achieve the intended target will get the authentication certificate from McNeel Asia.
课程报名 Register this course 课程即日开始报名, 开课一周前停止报名, 名额满提前报名结束. This course begin to sign up, stop sign up a week ago, with the quota ahead over.
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课程日期 Schedule 7/15-7/20 Beijing 北京 7/26-7/31 Shanghai 上海 7/07-7/12 Shenzhen 深圳
课程范例演示 Samples of Grasshopper course demo
Note: pls follow below comments by Jessesn to see the samples…
the Options. For example, if we look at the default settings in this order:
Population: Number of iterations / generation 50 - Galapagos tries 50 slider positions each generation. When it finishes 50, it looks at the results and takes from the best results based on your fitness.
Initial Boost: Factor for the first generation 2. You want to ensure Galapagos sees as much of the solution space as possible in order to not miss any potential solutions. The first generation is multiplied by this factor. If Population is 50, the first generation will be 50x2 = 100 slider positions.
Maintain and Inbreeding deal with what you keep between Generations.
Max Stagnant: Number of generations to try AFTER finding a better solution 50. If Galapagos finds a great solution in Generation 2 (Gen 0 = 100 tries, Gen 1 = 50 tries, Gen 2 = 50 tries) it will go another 50 Generations (50x50 tries) before it stops to ensure it did not miss anything.
Your solution space consists of 11 options, which is much less than any of the other parameters are suggesting. Galapagos flails wildly in your case because you told it to. You told it to try 50x50(+50 for initial boost) number of times to find the best value.
Hence why I do not think this is the best option. You said it, this is not an optimization problem. If it is not an optimization problem, why use a genetic algorithm solver which is predominantly used for optimizing parameters?
I wouldn't necessarily want to see the definition, I'm more curious about the data. For example, can you send the data for 10 structural members and some load cases? (again, I could be entirely oversimplifying it).
In any case, I changed Max. Stagnant to 5, Population to 11. So Galapagos will stop (5x11)+11 tries AFTER the best solution is found. It found the solution pretty quickly.…
Added by Luis Fraguada at 6:07am on September 7, 2016