title: Differential evolution for protein crystallographic optimizations.
abstract: 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.
authors.affiliation: ActiveSight and Molecular Images, 4045 Sorrento Valley Boulevard, San Diego, CA 92121, USA. dmcree@active-sight.com
authors.forename: Duncan E
authors.initials: DE
authors.lastname: McRee
mesh_terms: D000465:Algorithms; D018360:Crystallography, X-Ray / Q000706:statistics & numerical data* / Q000639:trends*; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins