Cartesian Genetic Programming Based Optimization and Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{conf/worldcist/SeoH14,

• author = "Kisung Seo and Byeongyong Hyeon",
• title = "Cartesian Genetic Programming Based Optimization and Prediction",
• booktitle = "WorldCIST 2014",
• year = "2014",
• editor = "Alvaro Rocha and Ana Maria Correia and Felix . B Tan and Karl . A Stroetmann",
• volume = "275",
• series = "Advances in Intelligent Systems and Computing",
• pages = "497--502",
• address = "Madeira Island, Portugal",
• month = apr # " 15-18",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming, gait optimisation, heavy rain prediction, symbolic regression",
• bibdate = "2015-02-04",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/conf/worldcist/worldcist2014-1.html#SeoH14",
• language = "English",
• isbn13 = "978-3-319-05950-1",
• DOI = "doi:10.1007/978-3-319-05951-8_47",
• abstract = "This paper introduces a CGP (Cartesian Genetic Programming) based optimisation and prediction techniques. In order to provide a superior search for optimisation and a robust model for prediction, a nonlinear and symbolic regression method using CGP is suggested. CGP uses as genotype a linear string of integers that are mapped to a directed graph. Therefore, some evolved modules for regression polynomials in CGP network can be shared and reused among multiple outputs for prediction of neighbourhood precipitation. To investigate the effectiveness of the proposed approach, experiments on gait generation for quadruped robots and prediction of heavy precipitation for local area of Korean Peninsular were executed.",

}

Genetic Programming entries for Kisung Seo Byeongyong Hyeon

Citations