ABSTRACT The genetic algorithms represent a family of algorithms using some of genetic principles being present in nature, in order to solve particular computational problems. These natural principles are: inheritance, crossover, mutation, survival of the fittest, migrations and so on. The paper describes the most important aspects of a genetic algorithm as a stochastic method for solving various classes of optimization problems. It also describes the basic genetic operator selection, crossover and mutation, serving for a new generation of individuals to achieve an optimal or a good enough solution of an optimization problem being in question.
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