Application of Genetic Programming for Electrical Engineering Predictive Modeling: A Review

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

@InCollection{Hosseini:2015:hbgpa,
  author =       "Seyyed Soheil Sadat Hosseini and Alireza Nemati",
  title =        "Application of Genetic Programming for Electrical
                 Engineering Predictive Modeling: A Review",
  booktitle =    "Handbook of Genetic Programming Applications",
  publisher =    "Springer",
  year =         "2015",
  editor =       "Amir H. Gandomi and Amir H. Alavi and Conor Ryan",
  chapter =      "6",
  pages =        "141--154",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-20882-4",
  DOI =          "doi:10.1007/978-3-319-20883-1_6",
  abstract =     "The purpose of having computers automatically resolve
                 problems is essential for machine learning, artificial
                 intelligence and a wide area covered by what Turing
                 called machine intelligence. Genetic programming (GP)
                 is an adaptable and strong evolutionary algorithm with
                 some features that can be very priceless and adequate
                 to get computers automatically to address problems
                 starting from a high-level statement of what to do.
                 Using the concept from natural evolution, GP begins
                 from an ooze of random computer programs and improve
                 them progressively through processes of mutation and
                 sexual recombination until solutions appear. All this
                 without the user needing to know or determine the form
                 or structure of solutions in advance. GP has produced a
                 plethora of human-competitive results and applications,
                 involving novel scientific discoveries and patent-able
                 inventions. The goal of this paper is to give an
                 introduction to the quickly developing field of GP. We
                 begin with a gentle introduction to the basic
                 representation, initialization and operators used in
                 GP, completed by a step by step description of their
                 use and application. Then, we progress to explain the
                 diversity of alternative representations for programs
                 and more advanced specializations of GP. Despite the
                 fact that this paper has been written with beginners
                 and practitioners in mind, for completeness we also
                 provide an outline of the theoretical aspect available
                 to date for GP.",
  notes =        "Author Affiliations: Department of Electrical
                 Engineering and Computer Science, University of Toledo,
                 Toledo, OH, 43606, USA",
}

Genetic Programming entries for S S Sadat Hosseini Alireza Nemati

Citations