Creation of numerical constants in robust gene expression programming

Fajfar, Iztok (author), Tuma, Tadej (author)

The problem of the creation of numerical constants has haunted the Genetic Programming (GP) community for a long time and is still considered one of the principal open research issues. Many problems tackled by GP include finding mathematical formulas, which often contain numerical constants. It is, however, a great challenge for GP to create highly accurate constants as their values are normally continuous, while GP is intrinsically suited for combinatorial optimization. The prevailing attempts to resolve this issue either employ separate real-valued local optimizers or special numeric mutations. While the former yield better accuracy than the latter, they add to implementation complexity and significantly increase computational cost. In this paper, we propose a special numeric crossover operator for use with Robust Gene Expression Programming (RGEP). RGEP is a type of genotype/phenotype evolutionary algorithm closely related to GP, but employing linear chromosomes. Using normalized least squares error as a fitness measure, we show that the proposed operator is significantly better in finding highly accurate solutions than the existing numeric mutation operators on several symbolic regression problems. Another two important advantages of the proposed operator are that it is extremely simple to implement, and it comes at no additional computational cost. The latter is true because the operator is integrated into an existing crossover operator and does not call for an additional cost function evaluation.

• Language: English
• Keywords: genetic programming, gene expression programming, genetic algorithms, genotype/phenotype evolutionary algorithms, symbolic regression, constant creation, ephemeral random constants, numeric mutation, numeric crossover, digit-wise crossover
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2018
• Number of pages: 15 str.
• Numbering: Vol. 20, iss. 10, art. 756
• PID: 20.500.12556/RUL-132103
• UDC: 004
• ISSN on article: 1099-4300
• DOI: 10.3390/e20100756
• COBISS.SI-ID: 12233812
• Publication date in RUL: 13.10.2021

Record is a part of a journal

• Title: Entropy
• Shortened title: Entropy
• Publisher: MDPI
• ISSN: 1099-4300
• COBISS.SI-ID: 515806233

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 01.10.2018

Secondary language

• Language: Slovenian
• Keywords: genetsko programiranje, programiranje z izraženimi geni, genetski algoritmi, evolucijski algoritmi s preslikavo iz genotipa v fenotip, simbolična regresija, generiranje konstant, prehodne naključne konstante, numerična mutacija, numerično križanje, križanje na nivoju števk

Projects

• Funder: ARRS - Slovenian Research Agency
• Project number: P2-0246
• Name: Algoritmi in optimizacijski postopki v telekomunikacijah