An Empirical Study on the Parametrization of Cartesian Genetic Programming

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

@InProceedings{Kaufmann:2017:GECCO,

• author = "Paul Kaufmann and Roman Kalkreuth",
• title = "An Empirical Study on the Parametrization of Cartesian Genetic Programming",
• booktitle = "Proceedings of the Genetic and Evolutionary Computation Conference Companion",
• series = "GECCO '17",
• year = "2017",
• isbn13 = "978-1-4503-4939-0",
• address = "Berlin, Germany",
• pages = "231--232",
• size = "2 pages",
• URL = "http://doi.acm.org/10.1145/3067695.3075980",
• DOI = "doi:10.1145/3067695.3075980",
• acmid = "3075980",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• month = "15-19 " # jul,
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• abstract = "Since its introduction two decades ago, the way researchers parametrised and optimized Cartesian Genetic Programming (CGP) remained almost unchanged. In this work we investigate non-standard parametrisations and optimization algorithms for CGP. We show that the conventional way of using CGP, i.e. configuring it as a single line optimized by an (1+4) Evolutionary Strategies-style search scheme, is a very good choice but that rectangular CGP geometries and more elaborate metaheuristics, such as Simulated Annealing, can lead to faster convergence rates.",
• notes = "Also known as \cite{Kaufmann:2017:ESP:3067695.3075980} GECCO-2017 A Recombination of the 26th International Conference on Genetic Algorithms (ICGA-2017) and the 22nd Annual Genetic Programming Conference (GP-2017)",

}

Genetic Programming entries for Paul Kaufmann Roman Tobias Kalkreuth

Citations