Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem

Created by W.Langdon from gp-bibliography.bib Revision:1.8592

@InProceedings{Borges:2010:ICEC,

• author = "Cruz Enrique Borges and Cesar L. Alonso and Jose Luis Montana and Marina {de la Cruz Echeandia} and Alfonso {Ortega de la Puente}",
• title = "Coevolutionary Architectures with Straight Line Programs for solving the Symbolic Regression Problem",
• booktitle = "Proceedings of the International Conference on Evolutionary Computation (ICEC 2010)",
• year = "2010",
• editor = "Joaquim Filipe and Janusz Kacprzyk",
• pages = "41--50",
• address = "Valencia, Spain",
• month = "24-26 " # oct,
• organisation = "INSTICC, AAAI, WfMC",
• publisher = "SciTePress",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-989-8425-31-7",
• URL = "http://paginaspersonales.deusto.es/cruz.borges/Papers/10ICEC.pdf",
• URL = "https://www.scitepress.org/PublishedPapers/2010/30751/",
• URL = "http://www.robinbye.com/files/publications/ICEC_2010.pdf",
• DOI = "doi:10.5220/0003075100410050",
• size = "10 pages",
• abstract = "To successfully apply evolutionary algorithms to the solution of increasingly complex problems we must develop effective techniques for evolving solutions in the form of interacting coadapted subcomponents. In this paper we present an architecture which involves cooperative coevolution of two subcomponents: a genetic program and an evolution strategy. As main difference with work previously done, our genetic program evolves straight line programs representing functional expressions, instead of tree structures. The evolution strategy searches for good values for the numerical terminal symbols used by those expressions. Experimentation has been performed over symbolic regression problem instances and the obtained results have been compared with those obtained by means of Genetic Programming strategies without coevolution. The results show that our coevolutionary architecture with straight line programs is capable to obtain better quality individuals than traditional genetic programming using the same amount of computational effort.",
• notes = "

  https://ecta.scitevents.org/ICEC2010/Program_Monday.htm

  Broken http://www.icec.ijcci.org/ICEC2010/home.asp Broken http://www.ijcci.org/Abstracts/2010/ICEC_2010_Abstracts.htm

  Also known as \cite{DBLP:conf/ijcci/BorgesAMEP10}",

}

Genetic Programming entries for Cruz Enrique Borges Cesar Luis Alonso Jose Luis Montana Arnaiz Marina de la Cruz Echeandia Alfonso Ortega de la Puente

Citations