Evolution of cartesian genetic programs capable of learning

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{DBLP:conf/gecco/KhanM09,
  author =       "Gul Muhammad Khan and Julian F. Miller",
  title =        "Evolution of cartesian genetic programs capable of
                 learning",
  booktitle =    "GECCO '09: Proceedings of the 11th Annual conference
                 on Genetic and evolutionary computation",
  year =         "2009",
  editor =       "Guenther Raidl and Franz Rothlauf and 
                 Giovanni Squillero and Rolf Drechsler and Thomas Stuetzle and 
                 Mauro Birattari and Clare Bates Congdon and 
                 Martin Middendorf and Christian Blum and Carlos Cotta and 
                 Peter Bosman and Joern Grahl and Joshua Knowles and 
                 David Corne and Hans-Georg Beyer and Ken Stanley and 
                 Julian F. Miller and Jano {van Hemert} and 
                 Tom Lenaerts and Marc Ebner and Jaume Bacardit and 
                 Michael O'Neill and Massimiliano {Di Penta} and Benjamin Doerr and 
                 Thomas Jansen and Riccardo Poli and Enrique Alba",
  pages =        "707--714",
  address =      "Montreal",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "8-12 " # jul,
  organisation = "SigEvo",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  isbn13 =       "978-1-60558-325-9",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  doi =          "doi:10.1145/1569901.1569999",
  abstract =     "We propose a new form of Cartesian Genetic Programming
                 (CGP) that develops into a computational network
                 capable of learning. The developed network architecture
                 is inspired by the brain. When the genetically encoded
                 programs are run, a networks develops consisting of
                 neurons, dendrites, axons, and synapses which can grow,
                 change or die. We have tested this approach on the task
                 of learning how to play checkers. The novelty of the
                 research lies mainly in two aspects: Firstly,
                 chromosomes are evolved that encode programs rather
                 than the network directly and when these programs are
                 executed they build networks which appear to be capable
                 of learning and improving their performance over time
                 solely through interaction with the environment.
                 Secondly, we show that we can obtain learning programs
                 much quicker through co-evolution in comparison to the
                 evolution of agents against a minimax based checkers
                 program. Also, co-evolved agents show significantly
                 increased learning capabilities compared to those that
                 were evolved to play against a minimax-based
                 opponent.",
  notes =        "GECCO-2009 A joint meeting of the eighteenth
                 international conference on genetic algorithms
                 (ICGA-2009) and the fourteenth annual genetic
                 programming conference (GP-2009).

                 ACM Order Number 910092.",
}

Genetic Programming entries for Gul Muhammad Khan Julian F Miller