This is a very simple genetic algorithm source code, developed by Denis Cormier (North Carolina State University), and the Sita s.r. aghavan (University of North Carolina at Charlotte) revision. The code is guaranteed to be as little as possible, and it's actually not necessary. To fix this code for a particular application, the user simply changes the definition of the constant and defines the "evaluation function". Notice that the design of the code is the maximum, and the target function can only be positive. There is no difference between the function value and the adaptive value of the individual. The system USES ratio selection, essence model, single point hybridization and uniform variation. If you replace uniform variation with Gaussian variation, you might get better results. The code doesn't have any graphics or even screen output, mainly to ensure high portability between platforms.

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simplegeneticalgorithmimplementation.doc

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