Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs

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

@InProceedings{Sikulova:2015:EuroGP,

• author = "Michaela Sikulova and Jiri Hulva and Lukas Sekanina",
• title = "Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs",
• booktitle = "18th European Conference on Genetic Programming",
• year = "2015",
• editor = "Penousal Machado and Malcolm I. Heywood and James McDermott and Mauro Castelli and Pablo Garcia-Sanchez and Paolo Burelli and Sebastian Risi and Kevin Sim",
• series = "LNCS",
• volume = "9025",
• publisher = "Springer",
• pages = "113--125",
• address = "Copenhagen",
• month = "8-10 " # apr,
• organisation = "EvoStar",
• keywords = "genetic algorithms, genetic programming, Cartesian genetic programming, Coevolution, Fitness prediction",
• isbn13 = "978-3-319-16500-4",
• DOI = "doi:10.1007/978-3-319-16501-1_10",
• abstract = "We investigate coevolutionary Cartesian genetic programming that coevolves fitness predictors in order to diminish the number of target objective vector (TOV) evaluations, needed to obtain a satisfactory solution, to reduce the computational cost of evolution. This paper introduces the use of coevolution of fitness predictors in CGP with a new type of indirectly encoded predictors. Indirectly encoded predictors are operated using the CGP and provide a variable number of TOVs used for solution evaluation during the coevolution. It is shown in five symbolic regression problems that the proposed predictors are able to adapt the size of TOVs array in response to a particular training data set.",
• notes = "Part of \cite{Machado:2015:GP} EuroGP'2015 held in conjunction with EvoCOP2015, EvoMusArt2015 and EvoApplications2015",

}

Genetic Programming entries for Michaela Sikulova Jiri Hulva Lukas Sekanina

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