Genetic algorithms (GA) are mimetic approaches to the “intelligence” behind natural evolution embodied by random selection and survival of the fittest, which seems to direct evolution in biological species. These algorithms make progress toward an optimum in a logic that mimics our understanding of genetic evolution. Hence, the term evolutionary computation, or evolutionary optimization, is often used. However, in some disciplines evolutionary optimization means incremental process set point or controller coefficient adjustment in a manner similar to a CHD search. Accordingly, I prefer the term GA over “evolutionary.”