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.

For more information, see our paper in the genetic programming track of GECCO, 2012 and our presentation PPT or PDF at the Symbolic Regression Workshop at GECCO 2012. If you are an MIT student and would like to discuss a UROP project in this research topic, please contact Dr. Erik Hemberg at