Coevolution of intelligent agents using cartesian genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Gul Muhammad Khan and Julian Francis Miller and 
                 David M. Halliday",
  title =        "Coevolution of intelligent agents using cartesian
                 genetic programming",
  booktitle =    "GECCO '07: Proceedings of the 9th annual conference on
                 Genetic and evolutionary computation",
  year =         "2007",
  editor =       "Dirk Thierens and Hans-Georg Beyer and 
                 Josh Bongard and Jurgen Branke and John Andrew Clark and 
                 Dave Cliff and Clare Bates Congdon and Kalyanmoy Deb and 
                 Benjamin Doerr and Tim Kovacs and Sanjeev Kumar and 
                 Julian F. Miller and Jason Moore and Frank Neumann and 
                 Martin Pelikan and Riccardo Poli and Kumara Sastry and 
                 Kenneth Owen Stanley and Thomas Stutzle and 
                 Richard A Watson and Ingo Wegener",
  volume =       "1",
  isbn13 =       "978-1-59593-697-4",
  pages =        "269--276",
  address =      "London",
  URL =          "",
  DOI =          "doi:10.1145/1276958.1277013",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Artificial Life, Evolutionary
                 Robotics, Adaptive Behaviour, Evolvable Hardware,
                 artificial neural networks, brain",
  abstract =     "A coevolutionary competitive learning environment for
                 two antagonistic agents is presented. The agents are
                 controlled by a new kind of computational network based
                 on a compartmentalised model of neurons. The genetic
                 basis of neurons is an important [27] and neglected
                 aspect of previous approaches. Accordingly, we have
                 defined a collection of chromosomes representing
                 various aspects of the neuron: soma, dendrites and axon
                 branches, and synaptic connections. Chromosomes are
                 represented and evolved using a form of genetic
                 programming (GP) known as Cartesian GP. The network
                 formed by running the chromosomal programs, has a
                 highly dynamic morphology in which neurons grow, and
                 die, and neurite branches together with synaptic
                 connections form and change in response to
                 environmental interactions. The idea of this paper is
                 to demonstrate the importance of the genetic transfer
                 of learned experience and life time learning. The
                 learning is a consequence of the complex dynamics
                 produced as a result of interaction (coevolution)
                 between two intelligent agents. Our results show that
                 both agents exhibit interesting learning
  notes =        "GECCO-2007 A joint meeting of the sixteenth
                 international conference on genetic algorithms
                 (ICGA-2007) and the twelfth annual genetic programming
                 conference (GP-2007).

                 ACM Order Number 910071",

Genetic Programming entries for Gul Muhammad Khan Julian F Miller David M Halliday