In a nutshell, the FlexGP project goal is scalable machine learning using genetic programming (GP). Genetic programming is a mature, robust multi-point search technique (inspired by evolution) which supports readable, and flexibly specified learning representations which can readily express linear or non-linear data relationships. It is well suited to parallelization and machine learning. It has a strong record in real world domains.
Learn more FlexGP project: http://flexgp.csail.mit.edu/index.php
The project offers multiple open source releases.: * */Evolutionary learners:/* this layer provides access to the learners so that one could run them on their desktop. See description of the learners here <http://flexgp.github.io/gp-learners/> and a tutorial to running them on multiple examples here <http://flexgp.github.io/gp-learners/blog.html> * */FlexGP: <http://flexgp.github.io/flexgp/>/* a cloud based platform for generating transparent non-linear large scale regression problems * */FCUBE: <http://flexgp.github.io/FCUBE/>/* A data parallel approach to building ensemble of classifiers