Method of Binary Analytic Programming to Look for Optimal Mathematical Expression

In the known methods of symbolical regression by search of the solution with the help of a genetic algorithm, there is a problem of crossover. Genetic programming performs a crossover only in certain points. Grammatical evolution often corrects a code after a crossover. Other methods of symbolical regression use excess elements in a code for elimination of this shortcoming. The work presents a new method of symbolic regression on base of binary computing trees. The method has no problems with a crossover. Method use a coding in the form of a set of integer numbers like analytic programming. The work describes the new method and some examples of codding for mathematical expressions. © 2017 The Authors.

Conference proceedings
Publisher
Elsevier B.V.
Language
English
Pages
597-604
Status
Published
Volume
103
Year
2017
Organizations
  • 1 Federal Research Center Computer Science and Control, Russian Academy of Sciences, 44, Vavilova str., Moscow, 119333, Russian Federation
  • 2 RUDN University, 6, Miklukho-Maklaya str., Moscow, 117198, Russian Federation
Keywords
analytic programming; genetic algorithm; genetic programming; symbolic regression
Date of creation
19.10.2018
Date of change
19.10.2018
Short link
https://repository.rudn.ru/en/records/article/record/6084/