Semantic Backpropagation for Designing Search Operators in Genetic Programming

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@Article{Pawlak:2014:ieeeEC,
  author =       "Tomasz P. Pawlak and Bartosz Wieloch and 
                 Krzysztof Krawiec",
  title =        "Semantic Backpropagation for Designing Search
                 Operators in Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2015",
  volume =       "19",
  number =       "3",
  pages =        "326--340",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, program
                 synthesis, semantics, reversible computing, problem
                 decomposition, mutation, geometric crossover",
  URL =          "http://dx.doi.org/10.1109/TEVC.2014.2321259",
  DOI =          "doi:10.1109/TEVC.2014.2321259",
  URL =          "http://www.cs.put.poznan.pl/tpawlak/?Semantic%20Backpropagation%20for%20Designing%20Search%20Operators%20in%20Genetic%20Programming,16",
  appendix_url = "http://www.cs.put.poznan.pl/tpawlak/files/research/2013SemanticBackpropagation/2013IEEE_Appendix.pdf",
  ISSN =         "1089-778X",
  abstract =     "In genetic programming, a search algorithm is expected
                 to produce a program that achieves the desired final
                 computation state (desired output). To reach that
                 state, an executing program needs to traverse certain
                 intermediate computation states. An evolutionary search
                 process is expected to autonomously discover such
                 states. This can be difficult for nontrivial tasks that
                 require long programs to be solved. The semantic
                 back-propagation algorithm proposed in this paper
                 heuristically inverts the execution of evolving
                 programs to determine the desired intermediate
                 computation states. Two search operators, Random
                 Desired Operator and Approximately Geometric Semantic
                 Crossover, use the intermediate states determined by
                 semantic backpropagation to define subtasks of the
                 original programming task, which are then solved using
                 an exhaustive search. The operators outperform the
                 standard genetic search operators and other
                 semantic-aware operators when compared on a suite of
                 symbolic regression and Boolean benchmarks. This result
                 and additional analysis conducted in this study
                 indicate that semantic back propagation helps evolution
                 at identifying the desired intermediate computation
                 states, and makes the search process more efficient.",
  notes =        "Java source code
                 http://www.cs.put.poznan.pl/tpawlak/files/research/2013SemanticBackpropagation/Evolution-src.zip
                 Also known as \cite{6808504}",
}

Genetic Programming entries for Tomasz Pawlak Bartosz Wieloch Krzysztof Krawiec

Citations