Accelerating convergence in cartesian genetic programming by using a new genetic operator

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Meier:2013:GECCO,

• author = "Andreas Meier and Mark Gonter and Rudolf Kruse",
• title = "Accelerating convergence in cartesian genetic programming by using a new genetic operator",
• booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference",
• year = "2013",
• editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi",
• isbn13 = "978-1-4503-1963-8",
• pages = "981--988",
• keywords = "genetic algorithms, genetic programming, cartesian genetic programming",
• month = "6-10 " # jul,
• organisation = "SIGEVO",
• address = "Amsterdam, The Netherlands",
• DOI = "doi:10.1145/2463372.2463481",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "Genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. In this paper, we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centres of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the Cartesian Genetic Programming algorithm and show that on our examples convergence is accelerated by over 50percent on average. We finish this paper with giving hints about parametrisation of the forking operator for other problems.",
• notes = "Also known as \cite{2463481} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)",

}

Genetic Programming entries for Andreas Meier Mark Gonter Rudolf Kruse

Citations