Hello Naman, welcome and thanks for getting in touch. I like the overall idea, but I'm not entirely sure which method you propose to implement; it sounds like you like to work on: Crow search algorithm, Grey wolf optimizer, Cuttlefish algorithm, Whale Optimization algorithm, Ant lion optimizer, personally I would focus on one or two methods that have a record to perform well on certain tasks. Let me know what you think.
Thanks, Marcus > On 18. Mar 2019, at 16:30, Naman Gupta <[email protected]> wrote: > > Hello Everyone. > > I am Naman Gupta, a computer science undergraduate student at MAIT, GGSIPU, > Delhi, India. I have been working on bio-inspired evolutionary algorithms for > the past 2 years and I have developed and implemented various optimized > versions of different bio-inspired algorithms in various fields including Ad > hoc networks, Medical Image Processing, and NLP. Some of my work has been > published in SCI-indexed journals (Q1 ranking). > > I have been working on bio-inspired algorithms namely, Crow search algorithm, > Grey wolf optimizer, Cuttlefish algorithm, Whale Optimization algorithm, Ant > lion optimizer, etc. and their usability in various domains. These algorithms > are inspired by the social behavior of animals in nature and provide far more > superior results when compared to the state of the algorithms (Evolutionary > and Genetic algorithms). Bio-inspired algorithms are gaining popularity day > by day because of their capability of finding solutions to NP-hard problems > and are being applied to a myriad of optimization engineering problems like > Thermal design, Structural optimization, Satellite layout design etc. I have > the statistics of over ten years representing the growing number of > applications of these algorithms. I have developed and modeled these > algorithms as feature selection algorithms (filter based and wrapper based) > which finds the most optimal feature subset from a large feature dataset. It > resolves the “curse of dimensionality” problem more efficiently and with less > computational time and higher classification accuracy. I have already > implemented the aforementioned algorithms in python during my research work. > > I am very much interested in contributing to mlpack in GSoC'19. Now, I want > to implement these algorithms in mlpack as feature selection mathods. These > algorithms are population-based, Meta-heuristic optimization techniques and > are simple, flexible, and avoids local optima. They search the global search > space in less computation time as compared to the traditional approaches Grid > search and Random Search. They will enhance the classification accuracy and > will reduce the computational time. > > It would be a great help if the mentors could provide me some insight into > this proposal idea. Can I propose this idea? Can you please suggest me > something to make it better. I will add more details, more functionality, and > features in the final proposal, this is just an abstract. I look forward to > hearing from you. > > > > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack
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