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

[Naman Gupta]

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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