Genetic global optimization algorithms

S. M Ermakov; D.N. Semenchikov

doi:10.1080/03610918.2019.1672739

Genetic global optimization algorithms

S. M Ermakov, D.N. Semenchikov

Результат исследований: Научные публикации в периодических изданиях › статья

1 Цитирования (Scopus)

Аннотация

Two new facts regarding genetic extremum search algorithms are presented. The first one, which is based on the use of a modification of the simulated annealing method, makes it possible to distinguish close extrema at the initial stage and indicates the strategy of behavior in the case of many equal extrema. The second fact (a lemma and its consequence) points out new methods for implementing the evolution of the covariance matrix. Numerical examples are given.

Язык оригинала	английский
Журнал	Communications in Statistics Part B: Simulation and Computation
DOI	https://doi.org/10.1080/03610918.2019.1672739
Состояние	Опубликовано - 7 окт 2019

Ключевые слова

Genetic algorithms
Simulated annealing
Global extremum
Experimental designing

Доступ к документу

10.1080/03610918.2019.1672739

Цитировать

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abstract = "Two new facts regarding genetic extremum search algorithms are presented. The first one, which is based on the use of a modification of the simulated annealing method, makes it possible to distinguish close extrema at the initial stage and indicates the strategy of behavior in the case of many equal extrema. The second fact (a lemma and its consequence) points out new methods for implementing the evolution of the covariance matrix. Numerical examples are given.",

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KW - Genetic algorithms

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KW - Global extremum

KW - Experimental designing

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