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On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems

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DOI: 10.4236/jsea.2011.48055    4,598 Downloads   9,978 Views   Citations


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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The authors declare no conflicts of interest.

Cite this paper

M. Bogdanović, "On Some Basic Concepts of Genetic Algorithms as a Meta-Heuristic Method for Solving of Optimization Problems," Journal of Software Engineering and Applications, Vol. 4 No. 8, 2011, pp. 482-486. doi: 10.4236/jsea.2011.48055.


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