Amodified probabilistic genetic algorithm for the solution of complex constrained optimization problems


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Abstract

A new algorithm for the solution of complex constrained optimization problems based on the probabilistic genetic algorithm with optimal solution prediction is proposed. The efficiency investigation results in comparison with standard genetic algorithm are presented.

About the authors

A Yu Vorozheikin

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

T N Gonchar

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

I A Panfilov

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

E A Sopov

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

S A Sopov

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

Siberian State Aerospace University named after academician M. F. Reshetnev, Russia, Krasnoyarsk

References

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  4. Сопов Е. А. О вероятностном генетическом алгоритме. Современные техника и технологии. В 2 т. Т. 2 / Е. А. Сопов // Томск: Изд-во Том. политехи, ун-та, 2004. С. 197-199.
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  6. Whitley, D. Building Better Test Functions/D. Whitley //Proc. of the Sixth Intern. Conf. on Genetic Algorithms and their Applications. Pittsburgh, PA, 1995.

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Copyright (c) 2009 Vorozheikin A.Y., Gonchar T.N., Panfilov I.A., Sopov E.A., Sopov S.A.

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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