Advanced techniques for the creation and propagation of modules in cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Kaufmann:2008:gecco,
  author =       "Paul Kaufmann and Marco Platzner",
  title =        "Advanced techniques for the creation and propagation
                 of modules in cartesian genetic programming",
  booktitle =    "GECCO '08: Proceedings of the 10th annual conference
                 on Genetic and evolutionary computation",
  year =         "2008",
  editor =       "Maarten Keijzer and Giuliano Antoniol and 
                 Clare Bates Congdon and Kalyanmoy Deb and Benjamin Doerr and 
                 Nikolaus Hansen and John H. Holmes and 
                 Gregory S. Hornby and Daniel Howard and James Kennedy and 
                 Sanjeev Kumar and Fernando G. Lobo and 
                 Julian Francis Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Jordan Pollack and Kumara Sastry and 
                 Kenneth Stanley and Adrian Stoica and El-Ghazali Talbi and 
                 Ingo Wegener",
  isbn13 =       "978-1-60558-130-9",
  pages =        "1219--1226",
  address =      "Atlanta, GA, USA",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2008/docs/p1219.pdf",
  DOI =          "doi:10.1145/1389095.1389334",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "12-16 " # jul,
  keywords =     "genetic algorithms, genetic programming, automatically
                 defined functions (ADFs), cartesian genetic
                 programming, crossover operator, embedded cartesian
                 genetic programming (ECGP), module acquisition",
  abstract =     "The choice of an appropriate hardware representation
                 model is key to successful evolution of digital
                 circuits. One of the most popular models is cartesian
                 genetic programming, which encodes an array of logic
                 gates into a chromosome. While several smaller circuits
                 have been successfully evolved on this model, it lacks
                 scalability. A recent approach towards scalable
                 hardware evolution is based on the automated creation
                 of modules from primitive gates.

                 In this paper, we present two novel approaches for
                 module creation, an age-based and a cone-based
                 technique. Further, we detail a cone-based crossover
                 operator for use with cartesian genetic programming. We
                 evaluate the different techniques and compare them with
                 related work. The results show that age-based module
                 creation is highly effective, while cone-based
                 approaches are only beneficial for regularly
                 structured, multiple output functions such as
                 multipliers.",
  notes =        "GECCO-2008 A joint meeting of the seventeenth
                 international conference on genetic algorithms
                 (ICGA-2008) and the thirteenth annual genetic
                 programming conference (GP-2008).

                 ACM Order Number 910081. Also known as
                 \cite{1389334}

                 Virtual FPGA. ECGP. One-row CGP (linear GP). Cones
                 (convergent paths). Cone based crossover. 5-even
                 parity, electromyographic signals. ES pop=5.",
}

Genetic Programming entries for Paul Kaufmann Marco Platzner

Citations