An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming

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@Article{Castelli:2017:CandC,
  author =       "Mauro Castelli and Leonardo Trujillo and 
                 Ivo Goncalves and Ales Popovic",
  title =        "An evolutionary system for the prediction of high
                 performance concrete strength based on semantic genetic
                 programming",
  journal =      "Computers and Concrete",
  year =         "2017",
  volume =       "19",
  number =       "6",
  pages =        "651--658",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, high
                 performance concrete, concrete strength, local search,
                 semantics",
  URL =          "http://www.techno-press.org/?page=container&journal=cac&volume=19&num=6",
  DOI =          "doi:10.12989/cac.2017.19.6.651",
  abstract =     "High-performance concrete, besides aggregate, cement,
                 and water, incorporates supplementary cementitious
                 materials, such as fly ash and blast furnace slag, and
                 chemical admixture, such as super-plasticiser. Hence,
                 it is a highly complex material and modelling its
                 behaviour represents a difficult task. This paper
                 presents an evolutionary system for the prediction of
                 high performance concrete strength. The proposed
                 framework blends a recently developed version of
                 genetic programming with a local search method. The
                 resulting system enables us to build a model that
                 produces an accurate estimation of the considered
                 parameter. Experimental results show the suitability of
                 the proposed system for the prediction of concrete
                 strength. The proposed method produces a lower error
                 with respect to the state-of-the art technique. The
                 paper provides two contributions: from the point of
                 view of the high performance concrete strength
                 prediction, a system able to outperform existing
                 state-of-the-art techniques is defined; from the
                 machine learning perspective, this case study shows
                 that including a local searcher in the geometric
                 semantic genetic programming system can speed up the
                 convergence of the search process.",
}

Genetic Programming entries for Mauro Castelli Leonardo Trujillo Ivo Goncalves Ales Popovic

Citations