Genetic programming with either stochastic or deterministic constant evaluation

Title:

Genetic programming with either stochastic or deterministic constant evaluation

Creator:

Hlavac, Vladimir

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:60e45bc6-8885-4a9d-b3b5-49ea1f64a6a6
uuid:60e45bc6-8885-4a9d-b3b5-49ea1f64a6a6
doi:10.14311/NNW.2018.28.007

Subject:

genetic programming, constant evaluation, gradient descent, and symbolic regression

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2018 Volume:28 | Number:2

Harvested from:

CDK

Metadata only:

false