Evolving controllers for simulated car racing using object oriented genetic programming

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

@InProceedings{1277271,
  author =       "Alexandros Agapitos and Julian Togelius and 
                 Simon Mark Lucas",
  title =        "Evolving controllers for simulated car racing using
                 object oriented 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 =       "2",
  isbn13 =       "978-1-59593-697-4",
  pages =        "1543--1550",
  address =      "London",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2007/docs/p1543.pdf",
  DOI =          "doi:10.1145/1276958.1277271",
  publisher =    "ACM Press",
  publisher_address = "New York, NY, USA",
  month =        "7-11 " # jul,
  organisation = "ACM SIGEVO (formerly ISGEC)",
  keywords =     "genetic algorithms, genetic programming, evolutionary
                 computer games, evolutionary robotics, homologous
                 uniform crossover, neural networks, object oriented,
                 subtree macro-mutation",
  abstract =     "The Probabilistic Adaptive Mapping Developmental
                 Genetic Programming (PAM DGP) algorithm that
                 cooperatively Co-evolves a population of adaptive
                 mappings and associated genotypes is used to learn
                 recursive solutions given a function set consisting of
                 general (not implicitly recursive) machine-language
                 instructions. PAM DGP using redundant encodings to
                 model the evolution of the biological genetic code is
                 found to more efficiently learn 2nd and 3rd order
                 recursive Fibonacci functions than related
                 developmental systems and traditional linear GP. PAM
                 DGP using redundant encoding is also demonstrated to
                 produce the semantically highest quality solutions for
                 all three recursive functions considered (Factorial,
                 2nd and 3rd order Fibonacci). PAM DGP is then shown to
                 have produced such solutions by evolving redundant
                 mappings to select and emphasise appropriate subsets of
                 the function set useful for producing the naturally
                 recursive solutions.",
  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 Alexandros Agapitos Julian Togelius Simon M Lucas

Citations