Open AccessDifferential evolution for protein crystallographic optimizations
Author(s) -
McRee Duncan E.
Publication year - 2004
Publication title -
acta crystallographica section d
Language(s) - English
Resource type - Journals
ISSN - 1399-0047
DOI - 10.1107/s0907444904025491
Subject(s) - computer science , algorithm , genetic programming , genetic algorithm , differential evolution , space (punctuation) , function (biology) , interdependence , differential (mechanical device) , theoretical computer science , machine learning , physics , biology , evolutionary biology , political science , law , operating system , thermodynamics
Genetic algorithms are powerful optimizers that have been underutilized in protein crystallography, given that many crystallographic problems have characteristics that would benefit from these algorithms: non‐linearity, interdependent parameters and a complex function landscape. These functions have been implemented for real‐space optimizations in a new fitting program, MIfit , for real‐space refinement of protein models and heavy‐atom searches. Some programming tips and examples will be presented here to aid others who might want to use genetic algorithms in their own work.