Abstract:
"
Evol-AutoNN is biomimicry approach to automate the neural network for tabular data
using the two biological concepts came from the nature. Biological evolution and the
neural network in the human brain are the two-concept used. As the domain of data
science keeps on growing, the demands for the tools that make the data science
approachable to non-experts will be ever increased. Biomimicry is a new procedure to
do the innovation which tries to give strong solutions to human problem by mimicking
the patterns and strategies identified in the nature.
Triumph of neural network depends on finding the best architecture. An architecture
which fundamentally contain the quantity of the neurons in the layer and the activation
functions used in them. As the fundamental can be seem as manageable but with
scaling up with the workflow makes complicate for both the technical and non technical stakeholders to do manually.
Evol-AutoNN is developed to solve the issues mentioned above. It provides novel
approach in finding the initial best setting for the user. The user also had the control
of selecting the budget which decide the time duration of computational power due to
the high concern about computational power. The user has the option in controlling
the ranges in the algorithm. EvolAutoNN imitates the process followed in both the
natural selection and genetics. It is available as open source which enables everyone
to access. "
