Genetic algorithms for numerical optimization

Zbigniew Michalewicz; Cezary Z. Janikow

doi:10.1007/BF01889983

Genetic algorithms for numerical optimization

Research output: Contribution to journal › Article › peer-review

Original language	American English
Journal	Statistics and Computing
Volume	1
DOIs	https://doi.org/10.1007/BF01889983
State	Published - Jan 12 1991
Externally published	Yes

Disciplines

Mathematics
Applied Mathematics
Artificial Intelligence and Robotics

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@article{9e81626c248b45518c8c9f1d3256fb1b,

title = "Genetic algorithms for numerical optimization",

author = "Zbigniew Michalewicz and Janikow, {Cezary Z.}",

note = "Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known.",

year = "1991",

month = jan,

day = "12",

doi = "10.1007/BF01889983",

language = "American English",

volume = "1",

journal = "Statistics and Computing",

}

TY - JOUR

T1 - Genetic algorithms for numerical optimization

AU - Michalewicz, Zbigniew

AU - Janikow, Cezary Z.

N1 - Genetic algorithms (GAs) are stochastic adaptive algorithms whose search method is based on simulation of natural genetic inheritance and Darwinian striving for survival. They can be used to find approximate solutions to numerical optimization problems in cases where finding the exact optimum is prohibitively expensive, or where no algorithm is known.

PY - 1991/1/12

Y1 - 1991/1/12

UR - https://link.springer.com/article/10.1007/BF01889983

U2 - 10.1007/BF01889983

DO - 10.1007/BF01889983

M3 - Article

VL - 1

JO - Statistics and Computing

JF - Statistics and Computing

ER -

Genetic algorithms for numerical optimization

Disciplines

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