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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    4,664 Downloads   10,110 Views   Citations

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.

Conflicts of Interest

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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