Semantic genetic programming for fast and accurate data knowledge discovery

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@Article{Castelli:2016:SEC,
  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Luca Manzoni and Ales Popovic",
  title =        "Semantic genetic programming for fast and accurate
                 data knowledge discovery",
  journal =      "Swarm and Evolutionary Computation",
  volume =       "26",
  year =         "2016",
  ISSN =         "2210-6502",
  DOI =          "doi:10.1016/j.swevo.2015.07.001",
  URL =          "http://www.sciencedirect.com/science/article/pii/S2210650215000516",
  abstract =     "Big data knowledge discovery emerged as an important
                 factor contributing to advancements in society at
                 large. Still, researchers continuously seek to advance
                 existing methods and provide novel ones for analysing
                 vast data sets to make sense of the data, extract
                 useful information, and build knowledge to inform
                 decision making. In the last few years, a very
                 promising variant of genetic programming was proposed:
                 geometric semantic genetic programming. Its difference
                 with the standard version of genetic programming
                 consists in the fact that it uses new genetic
                 operators, called geometric semantic operators, that,
                 acting directly on the semantics of the candidate
                 solutions, induce by definition a unimodal error
                 surface on any supervised learning problem,
                 independently from the complexity and size of the
                 underlying data set. This property should improve the
                 evolvability of genetic programming in presence of big
                 data and thus makes geometric semantic genetic
                 programming an extremely promising method for mining
                 vast amounts of data. Nevertheless, to the best of our
                 knowledge, no contribution has appeared so far to
                 employ this new technology to big data problems. This
                 paper intends to fill this gap. For the first time, in
                 fact, we show the effectiveness of geometric semantic
                 genetic programming on several complex real-life
                 problems, characterized by vast amounts of data, coming
                 from several different application domains.",
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Knowledge discovery",
}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Luca Manzoni Ales Popovic

Citations