Why evolution is not a good paradigm for program induction: a critique of genetic programming

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@InProceedings{WoodwardB:2009:GECa,
  author =       "John R. Woodward and Ruibin Bai",
  title =        "Why evolution is not a good paradigm for program
                 induction: a critique of genetic programming",
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  pages =        "593--600",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  URL =          "http://www.cs.nott.ac.uk/~jrw/publications/notEvolution.pdf",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.207.9680",
  DOI =          "doi:10.1145/1543834.1543915",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We revisit the roots of Genetic Programming (i.e.
                 Natural Evolution), and conclude that the mechanisms of
                 the process of evolution (i.e. selection, inheritance
                 and variation) are highly suited to the process;
                 genetic code is an effective transmitter of information
                 and crossover is an effective way to search through the
                 viable combinations. Evolution is not without its
                 limitations, which are pointed out, and it appears to
                 be a highly effective problem solver; however we
                 over-estimate the problem solving ability of evolution,
                 as it is often trying to solve 'self-imposed' survival
                 problems. We are concerned with the evolution of Turing
                 Equivalent programs (i.e. those with iteration and
                 memory). Each of the mechanisms which make evolution
                 work so well are examined from the perspective of
                 program induction. Computer code is not as robust as
                 genetic code, and therefore poorly suited to the
                 process of evolution, resulting in a insurmountable
                 landscape which cannot be navigated effectively with
                 current syntax based genetic operators. Crossover, has
                 problems being adopted in a computational setting,
                 primarily due to a lack of context of exchanged code. A
                 review of the literature reveals that evolved programs
                 contain at most two nested loops, indicating that a
                 glass ceiling to what can currently be accomplished.",
  notes =        "Also known as \cite{DBLP:conf/gecco/WoodwardB09a} part
                 of \cite{DBLP:conf/gec/2009}",
}

Genetic Programming entries for John R Woodward Ruibin Bai

Citations