“In this approach, we first define a search space for our candidate models and then let the algorithm do the searching and selecting. Our algorithm generates hundreds of models at a time, picks those that are most useful and then learns from this selection to go back and generate better …

"The conversion from the previous scheduler to Univa Grid Engine was virtually painless. Our users are happy that their hard-won knowledge continues to be relevant, significant scheduler bugs and vulnerabilities were fixed, and we also save on our own precious system administration time."