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Description: Genetic algorithms are simulated Darwinian natural selection of genetic selection and biological evolution of the computational model. Survival+ testing iterative search process is its core. Concrete into five, each of which is the process step implementation process: parameter code (the actual problem encoded genes), the initial group setting (ancestors), the design of fitness function (survival selection), design of genetic manipulation (genetic+ variation), the control design parameters (crossover rate of 0.2-0.99, mutation rate 0.001-0.1) .
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遗传算法C.doc
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