Accelerated Genetic Programming

author = "Vladimir Hlavac",

title = "Accelerated Genetic Programming",

booktitle = "MENDEL 2017, Recent Advances in Soft Computing",

year = "2017",

editor = "Radek Matousek",

volume = "837",

series = "AISC",

pages = "118--126",

address = "Brno, Czech Republic",

month = jun # " 20-22",

publisher = "Springer",

keywords = "genetic algorithms, genetic programming, Symbolic regression, Exponencionated gradient descent, Constant evaluation",

isbn13 = "978-3-319-97887-1",

URL = "

http://link.springer.com/chapter/10.1007/978-3-319-97888-8_9",

DOI = "

doi:10.1007/978-3-319-97888-8_9",

abstract = "Symbolic regression by the genetic programming is one of the options for obtaining a mathematical model for known data of output dependencies on inputs. Compared to neural networks (MLP), they can find a model in the form of a relatively simple mathematical relationship. The disadvantage is their computational difficulty. The following text describes several algorithm adjustments to enable acceleration and wider usage of the genetic programming. The performance of the resulting program was verified by several test functions containing several percent of the noise. The results are presented in graphs. The application is available at www.zpp.wz.cz/g.",

notes = "Published 2018",