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.

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