Differential evolution for protein crystallographic optimizations
Acta Crystallographica Section D
Volume 60, Issue 12-1, pages 2276–2279, December 2004
How to Cite
McRee, D. E. (2004), Differential evolution for protein crystallographic optimizations. Acta Crystallographica D, 60: 2276–2279. doi: 10.1107/S0907444904025491
- differential evolution;
- genetic algorithms;
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.