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.

• Evolutionary learners: this layer provides access to the learners so that one could run them on their desktop. See description of the learners here and a tutorial to running them on multiple examples here
• FlexGP: a cloud based platform for generating transparent non-linear large scale regression problems
• FCUBE: A data parallel approach to building ensemble of classifiers
• Feature learning: Evolutionary Feature Synthesis (EFS) generates accurate, readable, nonlinear features for tabular data.