TY - JOUR

T1 - Genetic global optimization algorithms

AU - Ermakov, S. M

AU - Semenchikov, D.N.

N1 - Publisher Copyright: © 2019 Taylor & Francis Group, LLC.

PY - 2019/10/7

Y1 - 2019/10/7

N2 - 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.

AB - 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.

KW - Genetic algorithms

KW - Simulated annealing

KW - Global extremum

KW - Experimental designing

KW - Experimental designing

KW - Genetic algorithms

KW - Global extremum

KW - Simulated annealing

UR - http://www.scopus.com/inward/record.url?scp=85074050770&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/96743957-cc32-3d71-80d1-55cca2e2b2a1/

U2 - 10.1080/03610918.2019.1672739

DO - 10.1080/03610918.2019.1672739

M3 - Article

VL - 51

SP - 1503

EP - 1512

JO - Communications in Statistics Part B: Simulation and Computation

JF - Communications in Statistics Part B: Simulation and Computation

SN - 0361-0918

IS - 4

ER -