Using GP is NEAT: Evolving Compositional Pattern Production Functions

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Filipe Assuncao and Nuno Lourenco and 
                 Penousal Machado and Bernardete Ribeiro",
  title =        "Using {GP} is {NEAT}: Evolving Compositional Pattern
                 Production Functions",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "3--18",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming, Grammatical
  isbn13 =       "978-3-319-77552-4",
  DOI =          "doi:10.1007/978-3-319-77553-1_1",
  abstract =     "The success of Artificial Neural Networks (ANNs)
                 highly depends on their architecture and on how they
                 are trained. However, making decisions regarding such
                 domain specific issues is not an easy task, and is
                 usually performed by hand, through an exhaustive
                 trial-and-error process. Over the years, researches
                 have developed and proposed methods to automatically
                 train ANNs. One example is the HyperNEAT algorithm,
                 which relies on NeuroEvolution of Augmenting Topologies
                 (NEAT) to create Compositional Pattern Production
                 Networks (CPPNs). CPPNs are networks that encode the
                 mapping between neuron positions and the synaptic
                 weight of the ANNs connection between those neurons.
                 Although this approach has obtained some success, it
                 requires meticulous parametrisation to work properly.
                 In this article we present a comparison of different
                 Evolutionary Computation methods to evolve
                 Compositional Pattern Production Functions: structures
                 that have the same goal as CPPNs, but that are encoded
                 as functions instead of networks. In addition to NEAT
                 three methods are used to evolve such functions:
                 Genetic Programming (GP), Grammatical Evolution, and
                 Dynamic Structured Grammatical Evolution. The results
                 show that GP is able to obtain competitive performance,
                 often surpassing the other methods, without requiring
                 the fine tuning of the parameters.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Filipe Assuncao Nuno Lourenco Penousal Machado Bernardete Ribeiro