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In biological systems, there is no guarantee that the most fit individual in a population will survive long enough to procreate because of environmental influences. For example, the most fit grizzly bear in Alaska could be killed by a hunter. While examples of this occur every day in the natural world, as engineers we may not want to lose the best induction motor design in our population. This has led to the elitism operator. This operator compares the most fit individual in a new population to the most fit individual in the previous population. If the best fitness in the new population is lower than the previous population, the most fit member of the previous population replaces a member of the new population. In this way, the best fitness in the population cannot go down. While this operator is distinctly nonbiological, it normally results in improved algorithm performance.