Scalable EDA-GP

 

Scalable Estimation of Distribution Genetic Programming

 

We are interested in genetic programming algorithm design which executes heritable genetic variation at the population level rather than individual level. Such algorithms are called Estimation of distribution GP. The key idea is to express the genetic dependencies of a fit subset of the current generation as a multivariate distribution which can be resampled in quest of better solutions. State of art EDA-GP algorithms use a prototype tree which limits their scalability. We are in the process of investigating how local patterns can replace and improve upon a prototype tree.

A secondary goal is to investigate how taking a multivariate distribution view of GP offers insight into its evolutionary dynamics.