Evolution Evolves with Autoconstruction

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@InProceedings{Spector:2016:GECCOcompA,
  author =       "Lee Spector and Nicholas Freitag McPhee and 
                 Thomas Helmuth and Maggie M. Casale and Julian Oks",
  title =        "Evolution Evolves with Autoconstruction",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1349--1356",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931727",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In autoconstructive evolutionary algorithms,
                 individuals implement not only candidate solutions to
                 specified computational problems, but also their own
                 methods for variation of offspring. This makes it
                 possible for the variation methods to themselves
                 evolve, which could, in principle, produce a system
                 with an enhanced capacity for adaptation and superior
                 problem solving power. Prior work on autoconsruction
                 has explored a range of system designs and their
                 evolutionary dynamics, but it has not solved hard
                 problems. Here we describe a new approach that can
                 indeed solve at least some hard problems. We present
                 the key components of this approach, including the use
                 of linear genomes for hierarchically structured
                 programs, a diversity-maintaining parent selection
                 algorithm, and the enforcement of diversification
                 constraints on offspring. We describe a software
                 synthesis benchmark problem that our new approach can
                 solve, and we present visualizations of data from
                 single successful runs of autoconstructive vs.
                 non-autoconstructive systems on this problem. While
                 anecdotal, the data suggests that variation methods,
                 and therefore significant aspects of the evolutionary
                 process, evolve over the course of the autoconstructive
                 runs.",
  notes =        "Distributed at GECCO-2016.",
}

Genetic Programming entries for Lee Spector Nicholas Freitag McPhee Thomas Helmuth Maggie M Casale Julian Oks

Citations