Automatic subroutine discovery in Genetic Programming (Text In Italian)

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@PhdThesis{Dessi:tesi,
  author =       "Antonello Dessi",
  title =        "Automatic subroutine discovery in Genetic Programming
                 (Text In Italian)",
  title_italian = "Scoperta automatica di subroutine in Programmazione
                 Genetica",
  school =       "University of Pisa",
  year =         "1998",
  type =         "tesi di laurea",
  address =      "Italy",
  keywords =     "genetic algorithms, genetic programming, subroutines,
                 ma, arl, aao",
  URL =          "http://web.mclink.it/MC2657/tesi.html",
  URL =          "http://web.mclink.it/MC2657/file/tesi.zip",
  size =         "292 pages",
  abstract =     "May 2018 Note this was translated from Italian by
                 Google Translate..... The developments of Artificial
                 Intelligence have shown that in dealing with complex
                 problems the key to success is the ability to break
                 them down into simpler subproblems, creating a
                 hierarchical and modular structure that allows to reach
                 a level of difficulty that can be faced. Whether you
                 consider Genetic Programming as an effective
                 possibility to reach a completely automatic programming
                 in the future, whether we consider it only an
                 alternative method for searching for algorithmic
                 solutions for a wide range of problems, the importance
                 of the management of the subroutines is therefore
                 fundamental. The Genetic Programming in its original
                 version fails to implement a real process of
                 hierarchical decomposition of the problems, but through
                 the introduction of the automatic discovery of the
                 subroutine this goal is achieved. The primary purpose
                 of the work carried out is therefore the analysis of
                 how this mechanism can be efficiently implemented, of
                 what the actual performances are observed, and which
                 are the possibilities for improvement.

                 The first chapter presents the Genetic Programming
                 framing it within the Evolutionary Algorithms ,
                 illustrating the numerous variants present in the
                 literature, the current research directions and the
                 possible applications of the model.

                 The second chapter illustrates the theoretical aspects
                 of Genetic Programming, with particular attention to
                 the theorems that try to model their behaviour (Scheme
                 and Price Theorem), highlighting their intrinsic
                 limits.

                 The third chapter analyses the different possibilities
                 concerning the introduction of subroutines in Genetic
                 Programming, to arrive at identifying the ARL model as
                 more promising . The various heuristics for the
                 automatic discovery of the subroutines are then
                 reviewed, and then proposed a new one ( salience ).
                 Finally the ARL algorithm is reviewed, criticizing some
                 aspects and proposing some variants, such as the use of
                 the mutation for the diffusion of the subroutines (
                 diffusion by mutation)) and an alternative method for
                 the dynamic era ( Maxfit ).

                 The fourth chapter illustrates the extensive
                 experimental analysis carried out, divided into three
                 phases: the first concerns the direct comparison
                 between the selection heuristics of the subroutines,
                 the second evaluates the effectiveness of the automatic
                 addition of the arguments to the new subroutines, the
                 third analyses the problem when it is appropriate to
                 insert new subroutines in the program population (
                 dynamic era). In this way the fundamental aspects of
                 the ARL algorithm are analysed and the efficacy of the
                 proposals advanced in the thesis: at the end of the
                 chapter are reported the relative conclusions and the
                 possible directions of future research.

                 The appendix contains further experimental data and the
                 source of the program developed specifically for the
                 needs of the thesis.",
  notes =        "In Italian

                 Includes source code in C. The C code has elements of
                 Module Acquisition (MA) and Adaptive Representation
                 with Learning (ARL)

                 relatori Antonina Starita and Antonella Giani",
}

Genetic Programming entries for Antonello Dessi

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