Hello Chintan, welcome and thanks for getting in touch.
> I would like to apply for the GSoC 2018 project on adding the Particle Swarm > Optimizer for constrained and unconstrained problems. I have some prior > experience working on PSO ([1], [2]) and would love to work on it yet again. > The > first link is the project where we train a basic Neural Net using PSO. It is > still a work in progress. The second link is just a bare bones implementation > of > PSO. Looks really interesting, the "Better initialization" point is definitely something that might affect the overall training time in a positive way. > I have followed the getting started links provided on the GSoC 2018 wiki page > and have built mlpack, and intend to tinker with it and go through the source > code once I'm done with the ongoing exams (by 10 March, 2018). Sounds good, best of luck with your exams. > Another interesting idea I would like to present is the use of gradient > descent > with PSO in a hybrid optimization approach ([3]: the repo belongs to my > project > mate). As of now it gives better training results, but neither of out > approaches > have been tested enough to actually comment on the matter yet. That is an interesting idea, I guess "Multi-objective Particle Swarm Optimization with Gradient Descent Search" by Li Ma and Babak Forouraghi could be interesting here as well. I hope anything I said was helpful; let me know if I should clarify anything. Thanks, Marcus > On 8. Mar 2018, at 14:37, Chintan Soni <[email protected]> wrote: > > Hello everyone, > > My name is Chintan Soni and I'm a 4th year CS student at Pune Institute of > Computer Technology, Pune, India. > > I would like to apply for the GSoC 2018 project on adding the Particle Swarm > Optimizer for constrained and unconstrained problems. > I have some prior experience working on PSO ([1], [2]) and would love to work > on it yet again. > The first link is the project where we train a basic Neural Net using PSO. It > is still a work in progress. The second link is just a bare bones > implementation of PSO. > > I am also currently interning at Nvidia Pune (till ~10 May, 2018) from where > I have experience working with somewhat complex C++ code and TMP, so > understanding the mlpack codebase shouldn't be much of a problem. > > I have followed the getting started links provided on the GSoC 2018 wiki page > and have built mlpack, and intend to tinker with it and go through the source > code once I'm done with the ongoing exams (by 10 March, 2018). > > Another interesting idea I would like to present is the use of gradient > descent with PSO in a hybrid optimization approach ([3]: the repo belongs to > my project mate). As of now it gives better training results, but neither of > out approaches have been tested enough to actually comment on the matter yet. > > Regarding the constrained optimization problem, I've come across an idea of > using PSO to solve Max-CSPs in the past (I cannot find a link to the paper as > of now but I'll try looking it up). Is that a step in the right direction? > Also, could you please provide references to other approaches for the same? > (Especially if you have anything specific in mind.) > > I will draft a proposal as soon as possible. Really looking forward to > working on the project. Thanks in advance for your help. > > Regards, > Chintan Soni > > Links used: > [1] https://github.com/chintans111/ANNPSO > <https://github.com/chintans111/ANNPSO> > [2] https://github.com/chintans111/SPSO <https://github.com/chintans111/SPSO> > [3] https://github.com/munagekar/nnpso <https://github.com/munagekar/nnpso> > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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