Genetic algorithms are based on the theory of the survival of the fittest. Key differences between traditional algorithms and genetic algorithms: Genetic algorithms use a population of designs. They don't start from a single point and then they optimize the function step after step. They start from several points inside the design space domain. Genetic algorithms have better probability to locate the global minimum of the function under consideration. Genetic algorithms require functional evaluation themselves. They do not require gradients, second derivatives. Genetic algorithms work on a coding of the design variables not the variables themselves. We can have different types of codings. Binary and real coding are common.

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