This article considers some of the connections between genetic algorithms (Gas)-search procedures based on the mechanics of natural selection and natural genetics-and human innovation. Simply stated, innovation has been a source of inspiration for thinking about genetic algorithms, and as the algorithms have improved, Gas have become increasingly interesting computational models of the processes of innovation. Th article reviews the basic of genetic algorithm operation and connects the basic mechanics to two processes Of innovation: continual improvement and discontinuous change. Thereafter, some of the technical lessons of Genetic algorithm processing are reviewed and their implications are briefly explored in the context of organiza- Tional change.
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