[mlpack] GSoc 2019 - mlpack : evolutionary algortihm project proposal idea discussion

[Marcus Edel]

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 <namangupta0227 at gmail.com> 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.
> 
> 
> 
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