Description: SGA (Simple Genetic Algorithm) is a powerful smart multi-variable optimization algorithm, which mimics the reproduction of law to be optimized. The SGA can be optimized variables, minimum, maximum, (when the function will last for the minimum) and supports the floating-point encoding, grey code, binary code round of betting method selection, tournament selection single-point crossover, uniform crossover , floating-point crossover single point mutation, floating-point mutation called Genetic (objective function name) the use of SGA, first of all need an objective function (like AimFunc.m), the fitness function returns the input variables to be optimized for the output variable x a fitness. Can be modified and then modify the place Genetic.m
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File list (Check if you may need any files):
Select.m
AimFunc.m
Code.m
Cross.m
Decode.m
Genetic.m
Mutation.m
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