GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY-Reference-Cited by-同舟云学术

GENETIC PROGRAMMING WITH LINEAR REPRESENTATION: A SURVEY

Published:2009-04 Issue:02 Volume:18 Page:197-238
ISSN:0218-2130
Container-title:International Journal on Artificial Intelligence Tools
language:en
Short-container-title:Int. J. Artif. Intell. Tools

Author:

OLTEAN MIHAI¹,GROŞAN CRINA¹,DIOŞAN LAURA¹,MIHĂILĂ CRISTINA¹

Affiliation:

1. Department of Computer Science, Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Kogălniceanu, 1, Cluj-Napoca, 400 084, România

Abstract

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evolution, Cartesian Genetic Programming and Stack-Based Genetic Programming. A complete description is provided for each method. The set of applications where the methods have been applied and several Internet sites with more information about them are also given.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Artificial Intelligence

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0218213009000111

Reference61 articles.

1. T. Perkis, IEEE WCCI 1 (IEEE Press, 1994) pp. 148–153.

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