bi-directional link, the link is unidirectional (downflow only), because of the use of proxies.
Matrix transforms and persistent constraints: I don't think this is true. The parts can have mates to other parts that preserve geometric relationships like 'coincident' , 'aligned' etc. These are essentially bi-directional. GH's algorithmic approach does not do relationships in the same / flexible way. In GH, the 'relationship' has to be part of the generation method that dependent on the creation sequence. I.e. draw line 2 perpendicularly from the end of point of line 1. If you are thinking about parts or assemblies sharing, or referencing parameters as part of the regen process, this is also possible. iLogic does this, and adds scripting. So does Catia. Inventor/iLogic can also access Excel and have all the parameter processing done centrally, if required.
Consequently, scripting the placement of components is irrelevant in GH, unless you decide that each component needs to be contained in its own separate file.
I wouldn't be too hasty here. Yes, you are right about compartmentalisation. I think this needs to happen with GH, in order to deal with scalability/everyday interoperability requirements. Confining projects to one script is not sustainable. MCAD apps have been doing this for ages with 'Relational Modeling'.The Adaptive Components placement example illustrates that it is beneficial to be able to script some 'hints' that can be used on placement of the component. Say, if your component requires points as inputs, then its should be able to find the nearest points to the cursor as it moves around. I think Aish's D# / DesignScript demo'd this kind of behaviour a few years ago. Similarly, Modo Toolpipe reminds me how a lot of UI based transactions can be captured as scripts (macro recorder etc). Allowing this input to be mixed in and/or extended by GH I think will yield a lot of 'modeling efficiency' around the edges. This is a (mis)using GH as an user-programmable 'jig' for placing/manipulating 'dumb' elements in Rhino. It may even give the 'dumb elements' a bit more 'intelligence' by leaving behind embedded attributes, like links to particular construction planes etc.Even if we confine ourselves to scripting. GH is a visual or graphic programming interface. A lot of 'insert and connect' tasks can be done more easily using graphic methods. If we need to select certain vertices on a mesh as inputs for, say, a facade panel, its going to be quicker to do this 'graphically' (like the AC example), then ferreting out the relevant indices in the data tree et al. The 'facade panel' script would then have some coding to filter/prompt the user as to what inputs were acceptable, and so on.
This also brings up the point that generating components and assemblies in MCAD is not as straightforward. In iParts and iAssemblies, each configuration needs to be generated as a "child" (the individual file needs to be created for each child) before those children can be used elsewhere.
Not sure what you mean here. If the i-parts are built up using sketches /profiles or other more rudimentary features (like Revits' profile/face etc family templates) then reuse should be fairly straight forward. I suppose you could make it like GH scripting, if you cut and paste or include script snippets that generate the desired Inventor features.
One of the reasons why the distributed file approach makes perfect sense in MCAD, is that in industry you deal with a finite set of objects. Generative tools are usually not a requirement. Most mechanical engineers, product engineers and machinists would never have any use for that.
I don't think this is true. Look at the automotive body design apps, which are mostly Catia based. All of the body parts are pretty much 'generative' and generated from splines, in a procedural way, using very similar approaches to GH. Or sheet metal design. It's not always about configuration of off-the-shelf items like bolts. And, the constraints manager is available to arbitrate which bit of script fires first, and your mundane workaday associative dimensions etc can update without getting run over by the DAG(s) :-)
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he picture (4).
Previously, I had a problem with generating intersections between the two directions of the beams, but a colleague helped me by extending beams, so there was no problem with lines of intersection. But this solution has generated curl (5) at the highest vertex geometry, which I ignored in order to repair it before printing, perhaps this mean my problem with my beam spread properly. Only when the beams is 19, does not jump no problem, but I still can not distribute them properly.
(1)
(2)
(3)
(4)
(5)
I tried to show as simply as possible by removing or signing my code in GHX file.
Thank you in advance for your help
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Crystallon is an open source project for creating lattice structures using Rhino and Grasshopper3D. The goal is to generate lattice structures within Rhino’s design environment without exporting t
lly it should not make much of a difference - random number generation is not affected, mutation also is not. crossover is a bit more tricky, I use Simulated Binary Crossover (SBX-20) which was introduced already in 1194:
Deb K., Agrawal R. B.: Simulated Binary Crossover for Continuous Search Space, inIITK/ME/SMD-94027, Convenor, Technical Reports, Indian Institue of Technology, Kanpur, India,November 1994
Abst ract. The success of binary-coded gene t ic algorithms (GA s) inproblems having discrete sear ch sp ace largely depends on the codingused to represent the prob lem variables and on the crossover ope ratorthat propagates buildin g blocks from pare nt strings to childrenst rings . In solving optimization problems having continuous searchspace, binary-co ded GAs discr et ize the search space by using a codingof the problem var iables in binary st rings. However , t he coding of realvaluedvari ables in finit e-length st rings causes a number of difficulties:inability to achieve arbit rary pr ecision in the obtained solution , fixedmapping of problem var iab les, inh eren t Hamming cliff problem associatedwit h binary coding, and processing of Holland 's schemata incont inuous search space. Although a number of real-coded GAs aredevelop ed to solve optimization problems having a cont inuous searchspace, the search powers of these crossover operators are not adequate .In t his paper , t he search power of a crossover operator is defined int erms of the probability of creating an arbitrary child solut ion froma given pair of parent solutions . Motivated by t he success of binarycodedGAs in discret e search space problems , we develop a real-codedcrossover (which we call the simulated binar y crossover , or SBX) operatorwhose search power is similar to that of the single-point crossoverused in binary-coded GAs . Simulation results on a number of realvaluedt est problems of varying difficulty and dimensionality suggestt hat the real-cod ed GAs with t he SBX operator ar e ab le to perform asgood or bet t er than binary-cod ed GAs wit h t he single-po int crossover.SBX is found to be particularly useful in problems having mult ip le optimalsolutions with a narrow global basin an d in prob lems where thelower and upper bo unds of the global optimum are not known a priori.Further , a simulation on a two-var iable blocked function showsthat the real-coded GA with SBX work s as suggested by Goldberg
and in most cases t he performance of real-coded GA with SBX is similarto that of binary GAs with a single-point crossover. Based onth ese encouraging results, this paper suggests a number of extensionsto the present study.
7. ConclusionsIn this paper, a real-coded crossover operator has been develop ed bas ed ont he search characte rist ics of a single-point crossover used in binary -codedGAs. In ord er to define the search power of a crossover operator, a spreadfactor has been introduced as the ratio of the absolute differences of thechildren points to that of the parent points. Thereaft er , the probabilityof creat ing a child point for two given parent points has been derived forthe single-point crossover. Motivat ed by the success of binary-coded GAsin problems wit h discrete sear ch space, a simul ated bin ary crossover (SBX)operator has been develop ed to solve problems having cont inuous searchspace. The SBX operator has search power similar to that of the single-po intcrossover.On a number of t est fun ctions, including De Jong's five te st fun ct ions, ithas been found that real-coded GAs with the SBX operator can overcome anumb er of difficult ies inherent with binary-coded GAs in solving cont inuoussearch space problems-Hamming cliff problem, arbitrary pr ecision problem,and fixed mapped coding problem. In the comparison of real-coded GAs wit ha SBX operator and binary-coded GAs with a single-point crossover ope rat or ,it has been observed that the performance of the former is better than thelatt er on continuous functions and the performance of the former is similarto the lat ter in solving discret e and difficult functions. In comparison withanother real-coded crossover operator (i.e. , BLX-0 .5) suggested elsewhere ,SBX performs better in difficult test functions. It has also been observedthat SBX is particularly useful in problems where the bounds of the optimum
point is not known a priori and wher e there are multi ple optima, of whichone is global.Real-coded GAs wit h t he SBX op erator have also been tried in solvinga two-variab le blocked function (the concept of blocked fun ctions was introducedin [10]). Blocked fun ct ions are difficult for real-coded GAs , becauselocal optimal points block t he progress of search to continue towards t heglobal optimal point . The simulat ion results on t he two-var iable blockedfunction have shown that in most occasions , the sea rch proceeds the way aspr edicted in [10]. Most importantly, it has been observed that the real-codedGAs wit h SBX work similar to that of t he binary-coded GAs wit h single-pointcrossover in overcoming t he barrier of the local peaks and converging to t heglobal bas in. However , it is premature to conclude whether real-coded GAswit h SBX op erator can overcome t he local barriers in higher-dimensionalblocked fun ct ions.These results are encour aging and suggest avenues for further research.Because the SBX ope rat or uses a probability distribut ion for choosing a childpo int , the real-coded GAs wit h SBX are one st ep ahead of the binary-codedGAs in te rms of ach ieving a convergence proof for GAs. With a direct probabilist ic relationship between children and parent points used in t his paper,cues from t he clas sical stochast ic optimization methods can be borrowed toachieve a convergence proof of GAs , or a much closer tie between the classicaloptimization methods and GAs is on t he horizon.
In short, according to the authors my SBX operator using real gene values is as good as older ones specially designed for discrete searches, and better in continuous searches. SBX as far as i know meanwhile is a standard general crossover operator.
But:
- there might be better ones out there i just havent seen yet. please tell me.
- besides tournament selection and mutation, crossover is just one part of the breeding pipeline. also there is the elite management for MOEA which is AT LEAST as important as the breeding itself.
- depending on the problem, there are almost always better specific ways of how to code the mutation and the crossover operators. but octopus is meant to keep it general for the moment - maybe there's a way for an interface to code those things yourself..!?
2) elite size = SPEA-2 archive size, yes. the rate depends on your convergence behaviour i would say. i usually start off with at least half the size of the population, but mostly the same size (as it is hard-coded in the new version, i just realize) is big enough.
4) the non-dominated front is always put into the archive first. if the archive size is exceeded, the least important individual (the significant strategy in SPEA-2) are truncated one by one until the size is reached. if it is smaller, the fittest dominated individuals are put into the elite. the latter happens in the beginning of the run, when the front wasn't discovered well yet.
3) yes it is. this is a custom implementation i figured out myself. however i'm close to have the HypE algorithm working in the new version, which natively has got the possibility to articulate perference relations on sets of solutions.
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