"GenBRDF: Discovering New Analytic BRDFs with Genetic Programming"
Adam Brady, Jason Lawrence, Pieter Peers, and Westley Weimer

ACM Transactions on Graphics, Volume 33, Issue 4, Article 114, August 2014
We present a framework for learning new analytic BRDF models through Genetic Programming that we call genBRDF. This approach to reflectance modeling can be seen as an extension of traditional methods that rely either on a phenomenological or empirical process. Our technique augments the human effort involved in deriving mathematical expressions that accurately characterize complex high-dimensional reflectance functions through a large-scale optimization. We present a number of analysis tools and data visualization techniques that are crucial to sifting through the large result sets produced by genBRDF in order to identify fruitful expressions. Additionally, we highlight several new models found by genBRDF that have not previously appeared in the BRDF literature. These new BRDF models are compact and more accurate than current state-of-the-art alternatives.

Supplementary Material
author = {Brady, Adam and Lawrence, Jason and Peers, Pieter and Weimer, Westley},
title = {GenBRDF: Discovering New Analytic BRDFs with Genetic Programming},
month = {August},
year = {2014},
articleno = {114},
journal = {ACM Transactions on Graphics},
volume = {33},
number = {4},
doi = {http://doi.acm.org/10.1145/2601097.2601193},