Semantic Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Moraglio:2015:GECCOcomp,
  author =       "Alberto Moraglio and Krzysztof Krawiec",
  title =        "Semantic Genetic Programming",
  booktitle =    "GECCO 2015 Advanced Tutorials",
  year =         "2015",
  editor =       "Anabela Simoes",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "603--627",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2756587",
  DOI =          "doi:10.1145/2739482.2756587",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Semantic genetic programming is a recent, rapidly
                 growing trend in Genetic Programming (GP) that aims at
                 opening the black box of the evaluation function and
                 make explicit use of more information on program
                 behaviour in the search. In the most common scenario of
                 evaluating a GP program on a set of input-output
                 examples (fitness cases), the semantic approach
                 characterizes program with a vector of outputs rather
                 than a single scalar value (fitness). The past research
                 on semantic GP has demonstrated that the additional
                 information obtained in this way facilitates designing
                 more effective search operators. In particular,
                 exploiting the geometric properties of the resulting
                 semantic space leads to search operators with
                 attractive properties, which have provably better
                 theoretical characteristics than conventional GP
                 operators. This in turn leads to dramatic improvements
                 in experimental comparisons.

                 The aim of the tutorial is to give a comprehensive
                 overview of semantic methods in genetic programming,
                 illustrate in an accessible way a formal geometric
                 framework for program semantics to design provably good
                 mutation and crossover operators for traditional GP
                 problem domains, and to analyse rigorously their
                 performance (runtime analysis). A number of real-world
                 applications of this framework will be also presented.
                 Other promising emerging approaches to semantics in GP
                 will be reviewed. In particular, the recent
                 developments in the behavioural programming, which aims
                 at characterizing the entire program behaviour (and not
                 only program outputs) will be covered as well. Current
                 challenges and future trends in semantic GP will be
                 identified and discussed.

                 Selected methods and concepts will be accompanied with
                 live software demonstrations. Also, efficient
                 implementation of semantic search operators may be
                 challenging. We will illustrate very efficient, concise
                 and elegant implementations of these operators, which
                 are available for download from the web.",
  notes =        "Also known as \cite{2756587} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Alberto Moraglio Krzysztof Krawiec

Citations