- author = "Rui L. Lopes and Ernesto Costa",
- title = "Genetic programming with genetic regulatory networks",
- 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 = "965--972",
- keywords = "genetic algorithms, genetic programming, inverted pendulum, artificial art",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2463372.2463488",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "Evolutionary Algorithms (EA) approach differently from
nature the genotype-phenotype relationship, and this
view is a recurrent issue among researchers. Recently,
some researchers have started exploring computationally
the new comprehension of the multitude of regulatory
mechanisms that are fundamental in both processes of
inheritance and of development in natural systems, by
trying to include those mechanisms in the EAs.
One of the first successful proposals was the Artificial Regulatory Network (ARN) model. Soon after some variants of the ARN, including different improvements over the base model, were tested. In this paper, we combine two of those alternatives, demonstrating experimentally how the resulting model can deal with complex problems, including those that have multiple outputs. The efficacy and efficiency of this variant are tested experimentally using two benchmark problems that show how we can evolve a controller or an artificial artist.",
- notes = "Also known as \cite{2463488} 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)",
