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This discussion of data acquisition and analysis considers the use of biological algorithms for data analysis. For laboratories that only deliver diagnostic testing results, increasing the TBs available in a database may be sufficient, but if multiple types of work are done, actual success is measured as maturity, ability, and understanding. Successful companies will have to enable data interpretation in order to be positioned for the enhanced ability to provide goods and services. They have to know not only who, what, where, and when, but also how and why. Several areas of biology other than biological plant growth can be used as parallels in the world of natural evolution and growth. Techniques very like those used by scientists for production of genetically changed, smarter, stronger, and more disease-resistant mice can be used to develop improved data and analysis methods. The process of problem analysis through genetic algorithms is demonstrated in the traveling salesman problem. To sum up, the total round trip distance predicted by each chromosome is determined, and those with shorter routes are regarded as the best fit. Two other biological parallels are described: learning and societal cooperation (swarm intelligence).
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