Evolution Strategies for Constants Optimization in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7954

@InProceedings{Alonso:2009:ICTAI,

• author = "Cesar L. Alonso and Jose Luis Montana and Cruz Enrique Borges",
• title = "Evolution Strategies for Constants Optimization in Genetic Programming",
• booktitle = "21st International Conference on Tools with Artificial Intelligence, ICTAI '09",
• year = "2009",
• month = nov,
• pages = "703--707",
• keywords = "genetic algorithms, genetic programming, computer program, constants optimization, evolutionary computation methods, learning problems, linear genetic programming approach, symbolic regression problem, regression analysis",
• DOI = "doi:10.1109/ICTAI.2009.35",
• ISSN = "1082-3409",
• abstract = "Evolutionary computation methods have been used to solve several optimization and learning problems. This paper describes an application of evolutionary computation methods to constants optimization in genetic programming. A general evolution strategy technique is proposed for approximating the optimal constants in a computer program representing the solution of a symbolic regression problem. The new algorithm has been compared with a recent linear genetic programming approach based on straight-line programs. The experimental results show that the proposed algorithm improves such technique.",
• notes = "Also known as \cite{5366517}",

}

Genetic Programming entries for Cesar Luis Alonso Jose Luis Montana Arnaiz Cruz Enrique Borges

Citations