Anthill Inside 2019

A conference on AI and Deep Learning





Feature selection and engineering using genetic algorithms and genetic programming

Submitted Apr 30, 2019

While feature selection is almost a solved problem in data science, feature engineering is still quite a mystery. In this talk I will outline a method that I use to solve feature engineering, with a goal to provide a generalized framework to tackle both feature engineering and selection simultaneoously.


The first few slides will talk about the application of genetic algorithms (GA) to feature selection. The next couple of slides will talk about advancements made to GAs by use of a multi-dimensional covariance map, a method that I developed. The next couple of slides will talk about genetic programming (GP) and how one can use the multi-dimensional covariance map to augment the convergence of GPs.


A good understanding of machine learning fundamentals

Speaker bio

I’m currently a principal data scientist at Intuit. A public but slightly dated bio is available here: An informal writeup on me is available here:


{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Hybrid access (members only)

Hosted by

Anthill Inside is a forum for conversations about risk mitigation and governance in Artificial Intelligence and Deep Learning. AI developers, researchers, startup founders, ethicists, and AI enthusiasts are encouraged to: more